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TimesFM Forecasting · FreedomIntelligence | DecimalAI

AI/MLby FreedomIntelligence

TimesFM Forecasting

Zero-shot time series forecasting with Google's TimesFM foundation model.

Never made a case worse

SkillScore

SkillScore64/100

Test results 64

Safety

3/3 safety checks passed

Code scan
Intent
Content

What it changes

5%64%of test cases passed +59 pts

Without skill1 of 22

With skill14 of 22

13 fixed1 passed either way8 still failing0 broke

Measured on gemini-3.6-flash7/26/2026v120 comparable

Track record

Agent runs: 0; All time

Use this skill

Your agent runs on

Adds every file the skill has, pinned so you can update it. No account needed.

pip install "decimalai>=0.15"
decimalai skills add timesfm-forecasting
claude --plugin-dir "$(git rev-parse --show-toplevel 2>/dev/null || pwd)/.agents"

The same install from Python, for a notebook or a setup script.

Install

pip install "decimalai>=0.15"

Add the skill

from decimalai import skillpkg

# Writes .agents/skills and .decimal/skills.toml. Safe to run twice.
result = skillpkg.add("timesfm-forecasting")
if result.exit_code:
    raise SystemExit(result.problems)

print("Added. Start Claude Code with the command below.")

Start Claude Code with it

claude --plugin-dir "$(git rev-parse --show-toplevel 2>/dev/null || pwd)/.agents"

Type these in a Claude Code session. No account or API key. It writes the skill and any files it bundles, and asks first when its safety rating isn't Passed.

Add the DecimalAI plugin, once

/plugin marketplace add decimal-labs/decimalai-claude-plugins
/plugin install decimalai@decimalai

Install this skill

/decimalai:install timesfm-forecasting

The plugin needs python3 and curl on your machine.

No Python or account. Claude Code picks it up in its next session.

Save it into your project

curl -fsSL --create-dirs -o .claude/skills/timesfm-forecasting/SKILL.md https://app.decimal.ai/s/timesfm-forecasting/SKILL.md

Copy SKILL.md

Save it in your project, as

.claude/skills/timesfm-forecasting/SKILL.md

This gets SKILL.md only. The skill's 5 other files are not included.

Other ways to add it

Claude Code docs

agent_config() hands your agent the skill. With no DecimalAI key it reads your project's files and sends nothing. Needs SDK 0.27 or newer.

Install

pip install "decimalai[claude-agent-sdk]"
decimalai skills add timesfm-forecasting

Add it to your agent

import asyncio

from claude_agent_sdk import ClaudeAgentOptions
from decimalai.claude_agent_sdk import agent_config

# With no DECIMAL_API_KEY this reads the skills your project lists, from disk.
support = agent_config("support", fallback="You are a helpful agent.")

async def main():
    question = "Hello!"
    async with support.run(question) as run:  # one user message, one run
        async for message in run.query(options=ClaudeAgentOptions(max_turns=6)):
            print(message)

# In a notebook or async code: await main()
asyncio.run(main())

Prefer its own loader? ClaudeAgentOptions(plugins=[skills_plugin()], skills="all") reads the same folder. Use one or the other: a skill delivered both ways reaches the prompt twice.

The same install from Python, for a notebook or a setup script. agent_config() hands your agent the skill. With no DecimalAI key it reads your project's files and sends nothing. Needs SDK 0.27 or newer.

Install

pip install "decimalai[claude-agent-sdk]"

Add it to your agent

from decimalai import skillpkg

# Writes .agents/skills and .decimal/skills.toml. Safe to run twice.
result = skillpkg.add("timesfm-forecasting")
if result.exit_code:
    raise SystemExit(result.problems)

import asyncio

from claude_agent_sdk import ClaudeAgentOptions
from decimalai.claude_agent_sdk import agent_config

# With no DECIMAL_API_KEY this reads the skills your project lists, from disk.
support = agent_config("support", fallback="You are a helpful agent.")

async def main():
    question = "Hello!"
    async with support.run(question) as run:  # one user message, one run
        async for message in run.query(options=ClaudeAgentOptions(max_turns=6)):
            print(message)

# In a notebook or async code: await main()
asyncio.run(main())

Prefer its own loader? ClaudeAgentOptions(plugins=[skills_plugin()], skills="all") reads the same folder. Use one or the other: a skill delivered both ways reaches the prompt twice.

No DecimalAI package. The whole skill goes in every prompt, and nothing pins or updates it.

Copy SKILL.md

Save it in your project, as

.agents/skills/timesfm-forecasting/SKILL.md

Give it to your agent

import asyncio
from pathlib import Path
from claude_agent_sdk import ClaudeAgentOptions, query

skill = Path(".agents/skills/timesfm-forecasting/SKILL.md").read_text()
options = ClaudeAgentOptions(system_prompt=f"You are a helpful agent.\n\n{skill}", max_turns=6)

async def main():
    async for message in query(prompt="Hello!", options=options):
        print(message)

# In a notebook or async code: await main()
asyncio.run(main())

This gets SKILL.md only. The skill's 5 other files are not included.

Other ways to add it

Claude Agent SDK docs

agent_config() hands your agent the skill. With no DecimalAI key it reads your project's files and sends nothing. Needs SDK 0.27 or newer.

Install

pip install "decimalai[openai-agents]"
decimalai skills add timesfm-forecasting

Add it to your agent

from agents import Agent, Runner
from decimalai.openai_agents import agent_config

# With no DECIMAL_API_KEY this reads the skills your project lists, from disk.
support = agent_config("support", fallback="You are a helpful agent.")

agent = Agent(
    name=support.name,
    instructions=support.instructions,  # your prompt, then the skill list
    tools=[*support.tools],             # your tools, then load_skill
)
# In a notebook or async code: result = await Runner.run(agent, "Hello!")
result = Runner.run_sync(agent, "Hello!")

Using SandboxAgent? capabilities=[Shell(), decimalai.openai_agents.sandbox_skills()] points its own loader at the same folder.

Install

pip install "decimalai[openai-agents]"

Add it to your agent

from decimalai import skillpkg

# Writes .agents/skills and .decimal/skills.toml. Safe to run twice.
result = skillpkg.add("timesfm-forecasting")
if result.exit_code:
    raise SystemExit(result.problems)

from agents import Agent, Runner
from decimalai.openai_agents import agent_config

# With no DECIMAL_API_KEY this reads the skills your project lists, from disk.
support = agent_config("support", fallback="You are a helpful agent.")

agent = Agent(
    name=support.name,
    instructions=support.instructions,  # your prompt, then the skill list
    tools=[*support.tools],             # your tools, then load_skill
)
# In a notebook or async code: result = await Runner.run(agent, "Hello!")
result = Runner.run_sync(agent, "Hello!")

Using SandboxAgent? capabilities=[Shell(), decimalai.openai_agents.sandbox_skills()] points its own loader at the same folder.

No DecimalAI package. The whole skill goes in every prompt, and nothing pins or updates it.

Copy SKILL.md

Save it in your project, as

.agents/skills/timesfm-forecasting/SKILL.md

Give it to your agent

from pathlib import Path
from agents import Agent, Runner

skill = Path(".agents/skills/timesfm-forecasting/SKILL.md").read_text()
agent = Agent(name="support", instructions=f"You are a helpful agent.\n\n{skill}")
# In a notebook or async code: result = await Runner.run(agent, "Hello!")
result = Runner.run_sync(agent, "Hello!")

This gets SKILL.md only. The skill's 5 other files are not included.

Other ways to add it

OpenAI Agents docs

agent_config() hands your agent the skill. With no DecimalAI key it reads your project's files and sends nothing. Needs SDK 0.27 or newer.

Install

pip install "decimalai[openai]"
decimalai skills add timesfm-forecasting

Add it to your agent

import json

from openai import OpenAI
from decimalai.openai import agent_config

client = OpenAI()
# With no DECIMAL_API_KEY this reads the skills your project lists, from disk.
support = agent_config("support", fallback="You are a helpful agent.")


def lookup_order(order_id: str) -> str:
    return f"Order {order_id} shipped yesterday."


# Your tools, as Chat Completions takes them, and what answers each.
TOOLS = [{"type": "function", "function": {
    "name": "lookup_order",
    "description": "Look up the status of an order.",
    "parameters": {"type": "object", "properties": {"order_id": {"type": "string"}},
                   "required": ["order_id"]},
}}]
FUNCTIONS = {"lookup_order": lookup_order}

question = "Where is order A1001?"
with support.run(question) as run:  # one user message, one run
    # run.system is your prompt, then the skill list.
    messages = [{"role": "system", "content": run.system}, run.message]
    while True:
        reply = client.chat.completions.create(
            model="gpt-5.4-mini",
            messages=messages,
            tools=[*TOOLS, *run.tools],  # your tools, then load_skill
        ).choices[0].message
        messages.append(reply)
        if not reply.tool_calls:
            break
        for call in reply.tool_calls:
            # load_skill is answered by the run; your own tools by FUNCTIONS.
            function = FUNCTIONS.get(call.function.name)
            messages.append(run.tool_result(call) or {
                "role": "tool", "tool_call_id": call.id,
                "content": function(**json.loads(call.function.arguments)) if function
                else f"There is no tool named {call.function.name!r}.",
            })
print(reply.content)

# The Responses API takes run.response_tools instead of run.tools.

Install

pip install "decimalai[openai]"

Add it to your agent

from decimalai import skillpkg

# Writes .agents/skills and .decimal/skills.toml. Safe to run twice.
result = skillpkg.add("timesfm-forecasting")
if result.exit_code:
    raise SystemExit(result.problems)

import json

from openai import OpenAI
from decimalai.openai import agent_config

client = OpenAI()
# With no DECIMAL_API_KEY this reads the skills your project lists, from disk.
support = agent_config("support", fallback="You are a helpful agent.")


def lookup_order(order_id: str) -> str:
    return f"Order {order_id} shipped yesterday."


# Your tools, as Chat Completions takes them, and what answers each.
TOOLS = [{"type": "function", "function": {
    "name": "lookup_order",
    "description": "Look up the status of an order.",
    "parameters": {"type": "object", "properties": {"order_id": {"type": "string"}},
                   "required": ["order_id"]},
}}]
FUNCTIONS = {"lookup_order": lookup_order}

question = "Where is order A1001?"
with support.run(question) as run:  # one user message, one run
    # run.system is your prompt, then the skill list.
    messages = [{"role": "system", "content": run.system}, run.message]
    while True:
        reply = client.chat.completions.create(
            model="gpt-5.4-mini",
            messages=messages,
            tools=[*TOOLS, *run.tools],  # your tools, then load_skill
        ).choices[0].message
        messages.append(reply)
        if not reply.tool_calls:
            break
        for call in reply.tool_calls:
            # load_skill is answered by the run; your own tools by FUNCTIONS.
            function = FUNCTIONS.get(call.function.name)
            messages.append(run.tool_result(call) or {
                "role": "tool", "tool_call_id": call.id,
                "content": function(**json.loads(call.function.arguments)) if function
                else f"There is no tool named {call.function.name!r}.",
            })
print(reply.content)

# The Responses API takes run.response_tools instead of run.tools.

No DecimalAI package. The whole skill goes in every prompt, and nothing pins or updates it.

Copy SKILL.md

Save it in your project, as

.agents/skills/timesfm-forecasting/SKILL.md

Give it to your agent

from pathlib import Path
from openai import OpenAI

client = OpenAI()
skill = Path(".agents/skills/timesfm-forecasting/SKILL.md").read_text()
reply = client.chat.completions.create(
    model="gpt-5.4-mini",
    messages=[
        {"role": "system", "content": f"You are a helpful agent.\n\n{skill}"},
        {"role": "user", "content": "Hello!"},
    ],
).choices[0].message

This gets SKILL.md only. The skill's 5 other files are not included.

Other ways to add it

OpenAI SDK docs

agent_config() hands your agent the skill. With no DecimalAI key it reads your project's files and sends nothing. Needs SDK 0.27 or newer.

Install

pip install "decimalai[anthropic]"
decimalai skills add timesfm-forecasting

Add it to your agent

import anthropic
from decimalai.anthropic import agent_config

client = anthropic.Anthropic()
# With no DECIMAL_API_KEY this reads the skills your project lists, from disk.
support = agent_config("support", fallback="You are a helpful agent.")

question = "Hello!"
with support.run(question) as run:  # one user message, one run
    answer = client.beta.messages.tool_runner(
        model="claude-haiku-5-5",
        max_tokens=4096,
        system=run.system,     # your prompt, then the skill list
        tools=[*run.tools],    # your tools, then load_skill
        messages=[{"role": "user", "content": question}],
    ).until_done()

Install

pip install "decimalai[anthropic]"

Add it to your agent

from decimalai import skillpkg

# Writes .agents/skills and .decimal/skills.toml. Safe to run twice.
result = skillpkg.add("timesfm-forecasting")
if result.exit_code:
    raise SystemExit(result.problems)

import anthropic
from decimalai.anthropic import agent_config

client = anthropic.Anthropic()
# With no DECIMAL_API_KEY this reads the skills your project lists, from disk.
support = agent_config("support", fallback="You are a helpful agent.")

question = "Hello!"
with support.run(question) as run:  # one user message, one run
    answer = client.beta.messages.tool_runner(
        model="claude-haiku-5-5",
        max_tokens=4096,
        system=run.system,     # your prompt, then the skill list
        tools=[*run.tools],    # your tools, then load_skill
        messages=[{"role": "user", "content": question}],
    ).until_done()

No DecimalAI package. The whole skill goes in every prompt, and nothing pins or updates it.

Copy SKILL.md

Save it in your project, as

.agents/skills/timesfm-forecasting/SKILL.md

Give it to your agent

from pathlib import Path
import anthropic

client = anthropic.Anthropic()
skill = Path(".agents/skills/timesfm-forecasting/SKILL.md").read_text()
reply = client.messages.create(
    model="claude-haiku-5-5",
    max_tokens=4096,
    system=f"You are a helpful agent.\n\n{skill}",
    messages=[{"role": "user", "content": "Hello!"}],
)

This gets SKILL.md only. The skill's 5 other files are not included.

Other ways to add it

Anthropic docs

agent_config() hands your agent the skill. With no DecimalAI key it reads your project's files and sends nothing. Needs SDK 0.27 or newer.

Install

pip install "decimalai[google-genai]"
decimalai skills add timesfm-forecasting

Add it to your agent

from google import genai
from google.genai import types
from decimalai.google_genai import agent_config

client = genai.Client()
# With no DECIMAL_API_KEY this reads the skills your project lists, from disk.
support = agent_config("support", fallback="You are a helpful agent.")


def lookup_order(order_id: str) -> str:
    return f"Order {order_id} shipped yesterday."


# Your functions, as declarations for the model, and what answers each.
TOOLS = [{"function_declarations": [{
    "name": "lookup_order",
    "description": "Look up the status of an order.",
    "parameters_json_schema": {"type": "object", "properties": {"order_id": {"type": "string"}},
                               "required": ["order_id"]},
}]}]
FUNCTIONS = {"lookup_order": lookup_order}

question = "Where is order A1001?"
with support.run(question) as run:  # one user message, one run
    config = types.GenerateContentConfig(
        system_instruction=run.system,     # your prompt, then the skill list
        tools=[*TOOLS, *run.tool_params],  # your functions, then load_skill
    )
    contents = [{"role": "user", "parts": [{"text": run.message}]}]
    while True:
        response = client.models.generate_content(
            model="gemini-3.5-flash", contents=contents, config=config,
        )
        if not response.function_calls:
            break
        contents.append(response.candidates[0].content)
        parts = []
        for call in response.function_calls:
            # load_skill is answered by the run; your own functions by FUNCTIONS.
            # A loop of your own rather than automatic function calling, which
            # raises KeyError when the model asks for a function you did not pass.
            function = FUNCTIONS.get(call.name)
            parts.append(run.tool_result(call) or {"function_response": {
                "name": call.name, "id": call.id,
                "response": {"result": function(**(call.args or {})) if function
                             else f"There is no function named {call.name!r}."},
            }})
        contents.append({"role": "user", "parts": parts})
print(response.text)

Install

pip install "decimalai[google-genai]"

Add it to your agent

from decimalai import skillpkg

# Writes .agents/skills and .decimal/skills.toml. Safe to run twice.
result = skillpkg.add("timesfm-forecasting")
if result.exit_code:
    raise SystemExit(result.problems)

from google import genai
from google.genai import types
from decimalai.google_genai import agent_config

client = genai.Client()
# With no DECIMAL_API_KEY this reads the skills your project lists, from disk.
support = agent_config("support", fallback="You are a helpful agent.")


def lookup_order(order_id: str) -> str:
    return f"Order {order_id} shipped yesterday."


# Your functions, as declarations for the model, and what answers each.
TOOLS = [{"function_declarations": [{
    "name": "lookup_order",
    "description": "Look up the status of an order.",
    "parameters_json_schema": {"type": "object", "properties": {"order_id": {"type": "string"}},
                               "required": ["order_id"]},
}]}]
FUNCTIONS = {"lookup_order": lookup_order}

question = "Where is order A1001?"
with support.run(question) as run:  # one user message, one run
    config = types.GenerateContentConfig(
        system_instruction=run.system,     # your prompt, then the skill list
        tools=[*TOOLS, *run.tool_params],  # your functions, then load_skill
    )
    contents = [{"role": "user", "parts": [{"text": run.message}]}]
    while True:
        response = client.models.generate_content(
            model="gemini-3.5-flash", contents=contents, config=config,
        )
        if not response.function_calls:
            break
        contents.append(response.candidates[0].content)
        parts = []
        for call in response.function_calls:
            # load_skill is answered by the run; your own functions by FUNCTIONS.
            # A loop of your own rather than automatic function calling, which
            # raises KeyError when the model asks for a function you did not pass.
            function = FUNCTIONS.get(call.name)
            parts.append(run.tool_result(call) or {"function_response": {
                "name": call.name, "id": call.id,
                "response": {"result": function(**(call.args or {})) if function
                             else f"There is no function named {call.name!r}."},
            }})
        contents.append({"role": "user", "parts": parts})
print(response.text)

No DecimalAI package. The whole skill goes in every prompt, and nothing pins or updates it.

Copy SKILL.md

Save it in your project, as

.agents/skills/timesfm-forecasting/SKILL.md

Give it to your agent

from pathlib import Path
from google import genai
from google.genai import types

client = genai.Client()
skill = Path(".agents/skills/timesfm-forecasting/SKILL.md").read_text()
response = client.models.generate_content(
    model="gemini-3.5-flash",
    contents="Hello!",
    config=types.GenerateContentConfig(system_instruction=f"You are a helpful agent.\n\n{skill}"),
)

This gets SKILL.md only. The skill's 5 other files are not included.

Other ways to add it

Gemini API docs

agent_config() hands your agent the skill. With no DecimalAI key it reads your project's files and sends nothing. Needs SDK 0.27 or newer.

Install

pip install "decimalai[langchain]" langchain-openai
decimalai skills add timesfm-forecasting

Add it to your agent

from langchain.agents import create_agent
from decimalai.langchain import agent_config

# With no DECIMAL_API_KEY this reads the skills your project lists, from disk.
support = agent_config("support", fallback="You are a helpful agent.")

agent = create_agent(
    "openai:gpt-5.4-mini",
    tools=[],                         # your tools; the middleware adds load_skill
    middleware=[support.middleware],  # your prompt, the skill list and load_skill
)
result = agent.invoke({"messages": [{"role": "user", "content": "Hello!"}]})

Install

pip install "decimalai[langchain]" langchain-openai

Add it to your agent

from decimalai import skillpkg

# Writes .agents/skills and .decimal/skills.toml. Safe to run twice.
result = skillpkg.add("timesfm-forecasting")
if result.exit_code:
    raise SystemExit(result.problems)

from langchain.agents import create_agent
from decimalai.langchain import agent_config

# With no DECIMAL_API_KEY this reads the skills your project lists, from disk.
support = agent_config("support", fallback="You are a helpful agent.")

agent = create_agent(
    "openai:gpt-5.4-mini",
    tools=[],                         # your tools; the middleware adds load_skill
    middleware=[support.middleware],  # your prompt, the skill list and load_skill
)
result = agent.invoke({"messages": [{"role": "user", "content": "Hello!"}]})

No DecimalAI package. The whole skill goes in every prompt, and nothing pins or updates it.

Copy SKILL.md

Save it in your project, as

.agents/skills/timesfm-forecasting/SKILL.md

Give it to your agent

from pathlib import Path
from langchain.agents import create_agent

skill = Path(".agents/skills/timesfm-forecasting/SKILL.md").read_text()
agent = create_agent("openai:gpt-5.4-mini", tools=[], system_prompt=f"You are a helpful agent.\n\n{skill}")
result = agent.invoke({"messages": [{"role": "user", "content": "Hello!"}]})

This gets SKILL.md only. The skill's 5 other files are not included.

Other ways to add it

LangChain docs

CrewAI's own skill loader reads the installed folder. No DecimalAI account needed.

Install

pip install "decimalai>=0.16" "crewai>=1.15.27"
decimalai skills add timesfm-forecasting

Add it to your agent

import decimalai.crewai
from crewai import Agent, Crew, Task

agent = Agent(
    role="Support", goal="Help the customer", backstory="A support agent.",
    llm=llm,                               # your own CrewAI LLM
    skills=decimalai.crewai.skills(),      # the installed folder
    tools=decimalai.crewai.skill_tools(),  # reads a skill's files, runs its scripts
)
task = Task(description="Hello!", expected_output="an answer", agent=agent)
Crew(agents=[agent], tasks=[task]).kickoff()

The same install from Python, for a notebook or a setup script. CrewAI's own skill loader reads the installed folder. No DecimalAI account needed.

Install

pip install "decimalai>=0.16" "crewai>=1.15.27"

Add it to your agent

from decimalai import skillpkg

# Writes .agents/skills and .decimal/skills.toml. Safe to run twice.
result = skillpkg.add("timesfm-forecasting")
if result.exit_code:
    raise SystemExit(result.problems)

import decimalai.crewai
from crewai import Agent, Crew, Task

agent = Agent(
    role="Support", goal="Help the customer", backstory="A support agent.",
    llm=llm,                               # your own CrewAI LLM
    skills=decimalai.crewai.skills(),      # the installed folder
    tools=decimalai.crewai.skill_tools(),  # reads a skill's files, runs its scripts
)
task = Task(description="Hello!", expected_output="an answer", agent=agent)
Crew(agents=[agent], tasks=[task]).kickoff()

No DecimalAI package. CrewAI's own loader reads the folder, and nothing pins or updates it.

Copy SKILL.md

Save it in your project, as

.agents/skills/timesfm-forecasting/SKILL.md

Give it to your agent

from pathlib import Path
from crewai import Agent, Crew, Task

agent = Agent(
    role="Support", goal="Help the customer", backstory="A support agent.",
    llm=llm,                         # your own CrewAI LLM
    skills=[Path(".agents/skills")],
)
task = Task(description="Hello!", expected_output="an answer", agent=agent)
Crew(agents=[agent], tasks=[task]).kickoff()

This gets SKILL.md only. The skill's 5 other files are not included.

Other ways to add it

CrewAI docs

agent_config() hands your agent the skill. With no DecimalAI key it reads your project's files and sends nothing. Needs SDK 0.27 or newer.

Install

pip install "decimalai[adk]"
decimalai skills add timesfm-forecasting

Add it to your agent

from decimalai.adk import DecimalAIPlugin, agent_config
from google.adk.agents import LlmAgent
from google.adk.apps import App

# With no DECIMAL_API_KEY this reads the skills your project lists, from disk.
support = agent_config("support", fallback="You are a helpful agent.")

agent = LlmAgent(
    name="support",          # ADK needs a Python identifier
    model="gemini-3.5-flash",
    instruction=support.instruction,  # your prompt, then the skill list
    tools=[*support.tools],           # your tools, then load_skill
)
# DecimalAIPlugin makes each invocation of runners built from this App one trace.
app = App(name="support", root_agent=agent, plugins=[DecimalAIPlugin()])

Prefer ADK's own loader? SkillToolset(skills=decimalai.adk.skills(), ...) reads the same folder. Use one or the other: a skill delivered both ways reaches the prompt twice.

Install

pip install "decimalai[adk]"

Add it to your agent

from decimalai import skillpkg

# Writes .agents/skills and .decimal/skills.toml. Safe to run twice.
result = skillpkg.add("timesfm-forecasting")
if result.exit_code:
    raise SystemExit(result.problems)

from decimalai.adk import DecimalAIPlugin, agent_config
from google.adk.agents import LlmAgent
from google.adk.apps import App

# With no DECIMAL_API_KEY this reads the skills your project lists, from disk.
support = agent_config("support", fallback="You are a helpful agent.")

agent = LlmAgent(
    name="support",          # ADK needs a Python identifier
    model="gemini-3.5-flash",
    instruction=support.instruction,  # your prompt, then the skill list
    tools=[*support.tools],           # your tools, then load_skill
)
# DecimalAIPlugin makes each invocation of runners built from this App one trace.
app = App(name="support", root_agent=agent, plugins=[DecimalAIPlugin()])

Prefer ADK's own loader? SkillToolset(skills=decimalai.adk.skills(), ...) reads the same folder. Use one or the other: a skill delivered both ways reaches the prompt twice.

No DecimalAI package. The whole skill goes in every prompt, and nothing pins or updates it.

Copy SKILL.md

Save it in your project, as

.agents/skills/timesfm-forecasting/SKILL.md

Give it to your agent

from pathlib import Path
from google.adk.agents import LlmAgent

skill = Path(".agents/skills/timesfm-forecasting/SKILL.md").read_text()
agent = LlmAgent(
    name="support",
    model="gemini-3.5-flash",
    instruction=lambda context: f"You are a helpful agent.\n\n{skill}",
)

This gets SKILL.md only. The skill's 5 other files are not included.

Other ways to add it

Google ADK docs

agent_config() hands your agent the skill. With no DecimalAI key it reads your project's files and sends nothing. Needs SDK 0.27 or newer.

Install

pip install "decimalai[pydantic-ai]"
decimalai skills add timesfm-forecasting

Add it to your agent

from pydantic_ai import Agent
from decimalai.pydantic_ai import agent_config

# With no DECIMAL_API_KEY this reads the skills your project lists, from disk.
support = agent_config("support", fallback="You are a helpful agent.")

agent = Agent(
    "openai:gpt-4o-mini",
    name=support.name,
    tools=[],                 # your tools; the capability adds load_skill
    capabilities=[support],   # your prompt, the skill list and load_skill
)
# In a notebook or async code: result = await agent.run("Hello!")
result = agent.run_sync("Hello!")

On the Pydantic AI harness, decimalai.pydantic_ai_harness.skills(project) uses its own loader instead.

Install

pip install "decimalai[pydantic-ai]"

Add it to your agent

from decimalai import skillpkg

# Writes .agents/skills and .decimal/skills.toml. Safe to run twice.
result = skillpkg.add("timesfm-forecasting")
if result.exit_code:
    raise SystemExit(result.problems)

from pydantic_ai import Agent
from decimalai.pydantic_ai import agent_config

# With no DECIMAL_API_KEY this reads the skills your project lists, from disk.
support = agent_config("support", fallback="You are a helpful agent.")

agent = Agent(
    "openai:gpt-4o-mini",
    name=support.name,
    tools=[],                 # your tools; the capability adds load_skill
    capabilities=[support],   # your prompt, the skill list and load_skill
)
# In a notebook or async code: result = await agent.run("Hello!")
result = agent.run_sync("Hello!")

On the Pydantic AI harness, decimalai.pydantic_ai_harness.skills(project) uses its own loader instead.

No DecimalAI package. The whole skill goes in every prompt, and nothing pins or updates it.

Copy SKILL.md

Save it in your project, as

.agents/skills/timesfm-forecasting/SKILL.md

Give it to your agent

from pathlib import Path
from pydantic_ai import Agent

skill = Path(".agents/skills/timesfm-forecasting/SKILL.md").read_text()
agent = Agent("openai:gpt-4o-mini", instructions=f"You are a helpful agent.\n\n{skill}")
# In a notebook or async code: result = await agent.run("Hello!")
result = agent.run_sync("Hello!")

This gets SKILL.md only. The skill's 5 other files are not included.

Other ways to add it

Pydantic AI docs

Deep Agents' own skill loader reads the installed folder. No DecimalAI account needed.

Install

pip install "decimalai>=0.16" deepagents
decimalai skills add timesfm-forecasting

Add it to your agent

import decimalai.deepagents
from deepagents import create_deep_agent

agent = create_deep_agent(model=model, **decimalai.deepagents.skills())
agent.invoke({"messages": [{"role": "user", "content": "Hello!"}]})

The default backend reads files but has no shell. To run a skill's scripts, pass skills(backend=LocalShellBackend(...)).

The same install from Python, for a notebook or a setup script. Deep Agents' own skill loader reads the installed folder. No DecimalAI account needed.

Install

pip install "decimalai>=0.16" deepagents

Add it to your agent

from decimalai import skillpkg

# Writes .agents/skills and .decimal/skills.toml. Safe to run twice.
result = skillpkg.add("timesfm-forecasting")
if result.exit_code:
    raise SystemExit(result.problems)

import decimalai.deepagents
from deepagents import create_deep_agent

agent = create_deep_agent(model=model, **decimalai.deepagents.skills())
agent.invoke({"messages": [{"role": "user", "content": "Hello!"}]})

The default backend reads files but has no shell. To run a skill's scripts, pass skills(backend=LocalShellBackend(...)).

No DecimalAI package. The whole skill goes in every prompt, and nothing pins or updates it.

Copy SKILL.md

Save it in your project, as

.agents/skills/timesfm-forecasting/SKILL.md

Give it to your agent

from pathlib import Path
from deepagents import create_deep_agent

skill = Path(".agents/skills/timesfm-forecasting/SKILL.md").read_text()
agent = create_deep_agent(model=model, system_prompt=f"You are a helpful agent.\n\n{skill}")
agent.invoke({"messages": [{"role": "user", "content": "Hello!"}]})

This gets SKILL.md only. The skill's 5 other files are not included.

Other ways to add it

Deep Agents docs

Its own skill loader reads the installed folder. No DecimalAI account needed.

Install

pip install "decimalai>=0.16" agent-framework
decimalai skills add timesfm-forecasting

Add it to your agent

import asyncio

from agent_framework import Agent
from decimalai.agent_framework import skills

agent = Agent(client=client, instructions="You are a helpful agent.", context_providers=[skills()])
asyncio.run(agent.run("Hello!"))

It caches skills by default, so restart the process after decimalai skills update.

The same install from Python, for a notebook or a setup script. Its own skill loader reads the installed folder. No DecimalAI account needed.

Install

pip install "decimalai>=0.16" agent-framework

Add it to your agent

from decimalai import skillpkg

# Writes .agents/skills and .decimal/skills.toml. Safe to run twice.
result = skillpkg.add("timesfm-forecasting")
if result.exit_code:
    raise SystemExit(result.problems)

import asyncio

from agent_framework import Agent
from decimalai.agent_framework import skills

agent = Agent(client=client, instructions="You are a helpful agent.", context_providers=[skills()])
asyncio.run(agent.run("Hello!"))

It caches skills by default, so restart the process after decimalai skills update.

No DecimalAI package. The whole skill goes in every prompt, and nothing pins or updates it.

Copy SKILL.md

Save it in your project, as

.agents/skills/timesfm-forecasting/SKILL.md

Give it to your agent

import asyncio
from pathlib import Path
from agent_framework import Agent

skill = Path(".agents/skills/timesfm-forecasting/SKILL.md").read_text()
agent = Agent(client=client, instructions=f"You are a helpful agent.\n\n{skill}")
asyncio.run(agent.run("Hello!"))

This gets SKILL.md only. The skill's 5 other files are not included.

Other ways to add it

Microsoft Agent Framework docs

Strands' own skill loader reads the installed folder. No DecimalAI account needed.

Install

pip install "decimalai>=0.16" strands-agents
decimalai skills add timesfm-forecasting

Add it to your agent

from decimalai.strands import skills
from strands import Agent
from strands.vended_tools import file_editor, shell

agent = Agent(
    model=model,                # always pass one: the default is Amazon Bedrock
    system_prompt="You are a helpful agent.",
    tools=[file_editor, shell],
    plugins=[skills()],
)
agent("Hello!")

Strands loads an agent's skills once, so build the agent again after decimalai skills update.

The same install from Python, for a notebook or a setup script. Strands' own skill loader reads the installed folder. No DecimalAI account needed.

Install

pip install "decimalai>=0.16" strands-agents

Add it to your agent

from decimalai import skillpkg

# Writes .agents/skills and .decimal/skills.toml. Safe to run twice.
result = skillpkg.add("timesfm-forecasting")
if result.exit_code:
    raise SystemExit(result.problems)

from decimalai.strands import skills
from strands import Agent
from strands.vended_tools import file_editor, shell

agent = Agent(
    model=model,                # always pass one: the default is Amazon Bedrock
    system_prompt="You are a helpful agent.",
    tools=[file_editor, shell],
    plugins=[skills()],
)
agent("Hello!")

Strands loads an agent's skills once, so build the agent again after decimalai skills update.

No DecimalAI package. The whole skill goes in every prompt, and nothing pins or updates it.

Copy SKILL.md

Save it in your project, as

.agents/skills/timesfm-forecasting/SKILL.md

Give it to your agent

from pathlib import Path
from strands import Agent

skill = Path(".agents/skills/timesfm-forecasting/SKILL.md").read_text()
agent = Agent(model=model, system_prompt=f"You are a helpful agent.\n\n{skill}")
agent("Hello!")

This gets SKILL.md only. The skill's 5 other files are not included.

Other ways to add it

Strands docs

Adds every file the skill has, pinned so you can update it. Cursor, Copilot, Cline and Warp read this folder by themselves.

pip install "decimalai>=0.15"
decimalai skills add timesfm-forecasting

No Python or account. Cursor, Copilot, Cline and Warp read this folder by themselves.

Save it into your project

curl -fsSL --create-dirs -o .agents/skills/timesfm-forecasting/SKILL.md https://app.decimal.ai/s/timesfm-forecasting/SKILL.md

Copy SKILL.md

Save it in your project, as

.agents/skills/timesfm-forecasting/SKILL.md

This gets SKILL.md only. The skill's 5 other files are not included.

Other ways to add it

Cursor and other editors docs

Fetch the file and give its text to your model as instructions. No account or package needed.

Get the file

curl -fsSL https://app.decimal.ai/s/timesfm-forecasting/SKILL.md

Read the summary as JSON

curl -fsSL https://app.decimal.ai/s/timesfm-forecasting.json

This gets SKILL.md only. The skill's 5 other files are not included.

Other ways to add it

Other setups docs

You don't need a DecimalAI account.

Version: v1, tested 7/26/2026
Source: FreedomIntelligence/OpenClaw-Medical-Skills ↗
License: Apache-2.0 license

Manage it in DecimalAI

Saved prompts and skills, pinned versions, and traces of this skill in your runs. DecimalAI does not run your agent.

Claude Code reads files from your project, so DecimalAI config does not apply there.

On Claude Agent SDK: save the skill to an agent here, set DECIMAL_API_KEY, and the same code uses it with no command to run. Changes you save reach the next run with no redeploy.

On OpenAI Agents: save the skill to an agent here, set DECIMAL_API_KEY, and the same code uses it with no command to run. Changes you save reach the next run with no redeploy.

On OpenAI SDK: save the skill to an agent here, set DECIMAL_API_KEY, and the same code uses it with no command to run. Changes you save reach the next run with no redeploy.

On Anthropic: save the skill to an agent here, set DECIMAL_API_KEY, and the same code uses it with no command to run. Changes you save reach the next run with no redeploy.

On Gemini API: save the skill to an agent here, set DECIMAL_API_KEY, and the same code uses it with no command to run. Changes you save reach the next run with no redeploy.

On LangChain: save the skill to an agent here, set DECIMAL_API_KEY, and the same code uses it with no command to run. Changes you save reach the next run with no redeploy.

On CrewAI: set DECIMAL_API_KEY and add decimalai.init(crewai=True) to record runs and skill use. The skill version stays in your repo.

On Google ADK: save the skill to an agent here, set DECIMAL_API_KEY, and the same code uses it with no command to run. Changes you save reach the next run with no redeploy.

On Pydantic AI: save the skill to an agent here, set DECIMAL_API_KEY, and the same code uses it with no command to run. Changes you save reach the next run with no redeploy.

On Deep Agents: set DECIMAL_API_KEY and add decimalai.init(langchain=True) to record runs and skill use. The skill version stays in your repo.

On Microsoft Agent Framework: set DECIMAL_API_KEY and add decimalai.init(otel=True, agent_name="support") to record runs and skill use. The skill version stays in your repo.

On Strands: set DECIMAL_API_KEY and add decimalai.init(otel=True, agent_name="support") to record runs and skill use. The skill version stays in your repo.

Editors read files from your project, so DecimalAI config does not apply there.

Anywhere else: record a use with decimalai.log_skill_activation() inside a trace.

Use this skill

SKILL.md

TimesFM Forecasting

Overview

TimesFM (Time Series Foundation Model) is a pretrained decoder-only foundation model developed by Google Research for time-series forecasting. It works zero-shot — feed it any univariate time series and it returns point forecasts with calibrated quantile prediction intervals, no training required.

This skill wraps TimesFM for safe, agent-friendly local inference. It includes a mandatory preflight system checker that verifies RAM, GPU memory, and disk space before the model is ever loaded so the agent never crashes a user's machine.

Key numbers: TimesFM 2.5 uses 200M parameters (~800 MB on disk, ~1.5 GB in RAM on CPU, ~1 GB VRAM on GPU). The archived v1/v2 500M-parameter model needs ~32 GB RAM. Always run the system checker first.

When to Use This Skill

Use this skill when:

Forecasting any univariate time series (sales, demand, sensor, vitals, price, weather)
You need zero-shot forecasting without training a custom model
You want probabilistic forecasts with calibrated prediction intervals (quantiles)
You have time series of any length (the model handles 1–16,384 context points)
You need to batch-forecast hundreds or thousands of series efficiently
You want a foundation model approach instead of hand-tuning ARIMA/ETS parameters

Do not use this skill when:

You need classical statistical models with coefficient interpretation → use statsmodels
You need time series classification or clustering → use aeon
You need multivariate vector autoregression or Granger causality → use statsmodels
Your data is tabular (not temporal) → use scikit-learn

Note on Anomaly Detection: TimesFM does not have built-in anomaly detection, but you can use the quantile forecasts as prediction intervals — values outside the 90% CI (q10–q90) are statistically unusual. See the examples/anomaly-detection/ directory for a full example.

⚠️ Mandatory Preflight: System Requirements Check

CRITICAL — ALWAYS run the system checker before loading the model for the first time.

python scripts/check_system.py

This script checks:

Available RAM — warns if below 4 GB, blocks if below 2 GB
GPU availability — detects CUDA/MPS devices and VRAM
Disk space — verifies room for the ~800 MB model download
Python version — requires 3.10+
Existing installation — checks if timesfm and torch are installed

Note: Model weights are NOT stored in this repository. TimesFM weights (~800 MB) download on-demand from HuggingFace on first use and cache in ~/.cache/huggingface/. The preflight checker ensures sufficient resources before any download begins.

flowchart TD
    accTitle: Preflight System Check
    accDescr: Decision flowchart showing the system requirement checks that must pass before loading TimesFM.

    start["🚀 Run check_system.py"] --> ram{"RAM ≥ 4 GB?"}
    ram -->|"Yes"| gpu{"GPU available?"}
    ram -->|"No (2-4 GB)"| warn_ram["⚠️ Warning: tight RAM<br/>CPU-only, small batches"]
    ram -->|"No (< 2 GB)"| block["🛑 BLOCKED<br/>Insufficient memory"]
    warn_ram --> disk
    gpu -->|"CUDA / MPS"| vram{"VRAM ≥ 2 GB?"}
    gpu -->|"CPU only"| cpu_ok["✅ CPU mode<br/>Slower but works"]
    vram -->|"Yes"| gpu_ok["✅ GPU mode<br/>Fast inference"]
    vram -->|"No"| cpu_ok
    gpu_ok --> disk{"Disk ≥ 2 GB free?"}
    cpu_ok --> disk
    disk -->|"Yes"| ready["✅ READY<br/>Safe to load model"]
    disk -->|"No"| block_disk["🛑 BLOCKED<br/>Need space for weights"]

    classDef ok fill:#dcfce7,stroke:#16a34a,stroke-width:2px,color:#14532d
    classDef warn fill:#fef9c3,stroke:#ca8a04,stroke-width:2px,color:#713f12
    classDef block fill:#fee2e2,stroke:#dc2626,stroke-width:2px,color:#7f1d1d
    classDef neutral fill:#f3f4f6,stroke:#6b7280,stroke-width:2px,color:#1f2937

    class ready,gpu_ok,cpu_ok ok
    class warn_ram warn
    class block,block_disk block
    class start,ram,gpu,vram,disk neutral

Hardware Requirements by Model Version

Model	Parameters	RAM (CPU)	VRAM (GPU)	Disk	Context
TimesFM 2.5 (recommended)	200M	≥ 4 GB	≥ 2 GB	~800 MB	up to 16,384
TimesFM 2.0 (archived)	500M	≥ 16 GB	≥ 8 GB	~2 GB	up to 2,048
TimesFM 1.0 (archived)	200M	≥ 8 GB	≥ 4 GB	~800 MB	up to 2,048

Recommendation: Always use TimesFM 2.5 unless you have a specific reason to use an older checkpoint. It is smaller, faster, and supports 8× longer context.

🔧 Installation

Step 1: Verify System (always first)

python scripts/check_system.py

Step 2: Install TimesFM

# Using uv (recommended by this repo)
uv pip install timesfm[torch]

# Or using pip
pip install timesfm[torch]

# For JAX/Flax backend (faster on TPU/GPU)
uv pip install timesfm[flax]

Step 3: Install PyTorch for Your Hardware

# CUDA 12.1 (NVIDIA GPU)
pip install torch>=2.0.0 --index-url https://download.pytorch.org/whl/cu121

# CPU only
pip install torch>=2.0.0 --index-url https://download.pytorch.org/whl/cpu

# Apple Silicon (MPS)
pip install torch>=2.0.0  # MPS support is built-in

Step 4: Verify Installation

import timesfm
import numpy as np
print(f"TimesFM version: {timesfm.__version__}")
print("Installation OK")

🎯 Quick Start

Minimal Example (5 Lines)

import torch, numpy as np, timesfm

torch.set_float32_matmul_precision("high")

model = timesfm.TimesFM_2p5_200M_torch.from_pretrained(
    "google/timesfm-2.5-200m-pytorch"
)
model.compile(timesfm.ForecastConfig(
    max_context=1024, max_horizon=256, normalize_inputs=True,
    use_continuous_quantile_head=True, force_flip_invariance=True,
    infer_is_positive=True, fix_quantile_crossing=True,
))

point, quantiles = model.forecast(horizon=24, inputs=[
    np.sin(np.linspace(0, 20, 200)),  # any 1-D array
])
# point.shape == (1, 24)        — median forecast
# quantiles.shape == (1, 24, 10) — 10th–90th percentile bands

Forecast from CSV

import pandas as pd, numpy as np

df = pd.read_csv("monthly_sales.csv", parse_dates=["date"], index_col="date")

# Convert each column to a list of arrays
inputs = [df[col].dropna().values.astype(np.float32) for col in df.columns]

point, quantiles = model.forecast(horizon=12, inputs=inputs)

# Build a results DataFrame
for i, col in enumerate(df.columns):
    last_date = df[col].dropna().index[-1]
    future_dates = pd.date_range(last_date, periods=13, freq="MS")[1:]
    forecast_df = pd.DataFrame({
        "date": future_dates,
        "forecast": point[i],
        "lower_80": quantiles[i, :, 2],  # 20th percentile
        "upper_80": quantiles[i, :, 8],  # 80th percentile
    })
    print(f"\n--- {col} ---")
    print(forecast_df.to_string(index=False))

Forecast with Covariates (XReg)

TimesFM 2.5+ supports exogenous variables through forecast_with_covariates(). Requires timesfm[xreg].

# Requires: uv pip install timesfm[xreg]
point, quantiles = model.forecast_with_covariates(
    inputs=inputs,
    dynamic_numerical_covariates={"price": price_arrays},
    dynamic_categorical_covariates={"holiday": holiday_arrays},
    static_categorical_covariates={"region": region_labels},
    xreg_mode="xreg + timesfm",  # or "timesfm + xreg"
)

Covariate Type	Description	Example
`dynamic_numerical`	Time-varying numeric	price, temperature, promotion spend
`dynamic_categorical`	Time-varying categorical	holiday flag, day of week
`static_numerical`	Per-series numeric	store size, account age
`static_categorical`	Per-series categorical	store type, region, product category

XReg Modes:

"xreg + timesfm" (default): TimesFM forecasts first, then XReg adjusts residuals
"timesfm + xreg": XReg fits first, then TimesFM forecasts residuals

See examples/covariates-forecasting/ for a complete example with synthetic retail data.

Anomaly Detection (via Quantile Intervals)

TimesFM does not have built-in anomaly detection, but the quantile forecasts naturally provide prediction intervals that can detect anomalies:

point, q = model.forecast(horizon=H, inputs=[values])

# 90% prediction interval
lower_90 = q[0, :, 1]  # 10th percentile
upper_90 = q[0, :, 9]  # 90th percentile

# Detect anomalies: values outside the 90% CI
actual = test_values  # your holdout data
anomalies = (actual < lower_90) | (actual > upper_90)

# Severity levels
is_warning = (actual < q[0, :, 2]) | (actual > q[0, :, 8])  # outside 80% CI
is_critical = anomalies  # outside 90% CI

Severity	Condition	Interpretation
Normal	Inside 80% CI	Expected behavior
Warning	Outside 80% CI	Unusual but possible
Critical	Outside 90% CI	Statistically rare (< 10% probability)

See examples/anomaly-detection/ for a complete example with visualization.

# Requires: uv pip install timesfm[xreg]
point, quantiles = model.forecast_with_covariates(
    inputs=inputs,
    dynamic_numerical_covariates={"temperature": temp_arrays},
    dynamic_categorical_covariates={"day_of_week": dow_arrays},
    static_categorical_covariates={"region": region_labels},
    xreg_mode="xreg + timesfm",  # or "timesfm + xreg"
)

📊 Understanding the Output

Quantile Forecast Structure

TimesFM returns (point_forecast, quantile_forecast):

point_forecast: shape (batch, horizon) — the median (0.5 quantile)
quantile_forecast: shape (batch, horizon, 10) — ten slices:

Index	Quantile	Use
0	Mean	Average prediction
1	0.1	Lower bound of 80% PI
2	0.2	Lower bound of 60% PI
3	0.3	—
4	0.4	—
5	0.5	Median (= `point_forecast`)
6	0.6	—
7	0.7	—
8	0.8	Upper bound of 60% PI
9	0.9	Upper bound of 80% PI

Extracting Prediction Intervals

point, q = model.forecast(horizon=H, inputs=data)

# 80% prediction interval (most common)
lower_80 = q[:, :, 1]  # 10th percentile
upper_80 = q[:, :, 9]  # 90th percentile

# 60% prediction interval (tighter)
lower_60 = q[:, :, 2]  # 20th percentile
upper_60 = q[:, :, 8]  # 80th percentile

# Median (same as point forecast)
median = q[:, :, 5]

flowchart LR
    accTitle: Quantile Forecast Anatomy
    accDescr: Diagram showing how the 10-element quantile vector maps to prediction intervals.

    input["📈 Input Series<br/>1-D array"] --> model["🤖 TimesFM<br/>compile + forecast"]
    model --> point["📍 Point Forecast<br/>(batch, horizon)"]
    model --> quant["📊 Quantile Forecast<br/>(batch, horizon, 10)"]
    quant --> pi80["80% PI<br/>q[:,:,1] – q[:,:,9]"]
    quant --> pi60["60% PI<br/>q[:,:,2] – q[:,:,8]"]
    quant --> median["Median<br/>q[:,:,5]"]

    classDef data fill:#dbeafe,stroke:#2563eb,stroke-width:2px,color:#1e3a5f
    classDef model fill:#f3e8ff,stroke:#9333ea,stroke-width:2px,color:#581c87
    classDef output fill:#dcfce7,stroke:#16a34a,stroke-width:2px,color:#14532d

    class input data
    class model model
    class point,quant,pi80,pi60,median output

🔧 ForecastConfig Reference

All forecasting behavior is controlled by timesfm.ForecastConfig:

timesfm.ForecastConfig(
    max_context=1024,                    # Max context window (truncates longer series)
    max_horizon=256,                     # Max forecast horizon
    normalize_inputs=True,               # Normalize inputs (RECOMMENDED for stability)
    per_core_batch_size=32,              # Batch size per device (tune for memory)
    use_continuous_quantile_head=True,   # Better quantile accuracy for long horizons
    force_flip_invariance=True,          # Ensures f(-x) = -f(x) (mathematical consistency)
    infer_is_positive=True,              # Clamp forecasts ≥ 0 when all inputs > 0
    fix_quantile_crossing=True,          # Ensure q10 ≤ q20 ≤ ... ≤ q90
    return_backcast=False,               # Return backcast (for covariate workflows)
)

Parameter	Default	When to Change
`max_context`	0	Set to match your longest historical window (e.g., 512, 1024, 4096)
`max_horizon`	0	Set to your maximum forecast length
`normalize_inputs`	False	Always set True — prevents scale-dependent instability
`per_core_batch_size`	1	Increase for throughput; decrease if OOM
`use_continuous_quantile_head`	False	Set True for calibrated prediction intervals
`force_flip_invariance`	True	Keep True unless profiling shows it hurts
`infer_is_positive`	True	Set False for series that can be negative (temperature, returns)
`fix_quantile_crossing`	False	Set True to guarantee monotonic quantiles

📋 Common Workflows

Workflow 1: Single Series Forecast

flowchart TD
    accTitle: Single Series Forecast Workflow
    accDescr: Step-by-step workflow for forecasting a single time series with system checking.

    check["1. Run check_system.py"] --> load["2. Load model<br/>from_pretrained()"]
    load --> compile["3. Compile with ForecastConfig"]
    compile --> prep["4. Prepare data<br/>pd.read_csv → np.array"]
    prep --> forecast["5. model.forecast()<br/>horizon=N"]
    forecast --> extract["6. Extract point + PI"]
    extract --> plot["7. Plot or export results"]

    classDef step fill:#f3f4f6,stroke:#6b7280,stroke-width:2px,color:#1f2937
    class check,load,compile,prep,forecast,extract,plot step

import torch, numpy as np, pandas as pd, timesfm

# 1. System check (run once)
# python scripts/check_system.py

# 2-3. Load and compile
torch.set_float32_matmul_precision("high")
model = timesfm.TimesFM_2p5_200M_torch.from_pretrained(
    "google/timesfm-2.5-200m-pytorch"
)
model.compile(timesfm.ForecastConfig(
    max_context=512, max_horizon=52, normalize_inputs=True,
    use_continuous_quantile_head=True, fix_quantile_crossing=True,
))

# 4. Prepare data
df = pd.read_csv("weekly_demand.csv", parse_dates=["week"])
values = df["demand"].values.astype(np.float32)

# 5. Forecast
point, quantiles = model.forecast(horizon=52, inputs=[values])

# 6. Extract prediction intervals
forecast_df = pd.DataFrame({
    "forecast": point[0],
    "lower_80": quantiles[0, :, 1],
    "upper_80": quantiles[0, :, 9],
})

# 7. Plot
import matplotlib.pyplot as plt
fig, ax = plt.subplots(figsize=(12, 5))
ax.plot(values[-104:], label="Historical")
x_fc = range(len(values[-104:]), len(values[-104:]) + 52)
ax.plot(x_fc, forecast_df["forecast"], label="Forecast", color="tab:orange")
ax.fill_between(x_fc, forecast_df["lower_80"], forecast_df["upper_80"],
                alpha=0.2, color="tab:orange", label="80% PI")
ax.legend()
ax.set_title("52-Week Demand Forecast")
plt.tight_layout()
plt.savefig("forecast.png", dpi=150)
print("Saved forecast.png")

Workflow 2: Batch Forecasting (Many Series)

import pandas as pd, numpy as np

# Load wide-format CSV (one column per series)
df = pd.read_csv("all_stores.csv", parse_dates=["date"], index_col="date")
inputs = [df[col].dropna().values.astype(np.float32) for col in df.columns]

# Forecast all series at once (batched internally)
point, quantiles = model.forecast(horizon=30, inputs=inputs)

# Collect results
results = {}
for i, col in enumerate(df.columns):
    results[col] = {
        "forecast": point[i].tolist(),
        "lower_80": quantiles[i, :, 1].tolist(),
        "upper_80": quantiles[i, :, 9].tolist(),
    }

# Export
import json
with open("batch_forecasts.json", "w") as f:
    json.dump(results, f, indent=2)
print(f"Forecasted {len(results)} series → batch_forecasts.json")

Workflow 3: Evaluate Forecast Accuracy

import numpy as np

# Hold out the last H points for evaluation
H = 24
train = values[:-H]
actual = values[-H:]

point, quantiles = model.forecast(horizon=H, inputs=[train])
pred = point[0]

# Metrics
mae = np.mean(np.abs(actual - pred))
rmse = np.sqrt(np.mean((actual - pred) ** 2))
mape = np.mean(np.abs((actual - pred) / actual)) * 100

# Prediction interval coverage
lower = quantiles[0, :, 1]
upper = quantiles[0, :, 9]
coverage = np.mean((actual >= lower) & (actual <= upper)) * 100

print(f"MAE:  {mae:.2f}")
print(f"RMSE: {rmse:.2f}")
print(f"MAPE: {mape:.1f}%")
print(f"80% PI Coverage: {coverage:.1f}% (target: 80%)")

⚙️ Performance Tuning

GPU Acceleration

import torch

# Check GPU availability
if torch.cuda.is_available():
    print(f"GPU: {torch.cuda.get_device_name(0)}")
    print(f"VRAM: {torch.cuda.get_device_properties(0).total_mem / 1e9:.1f} GB")
elif hasattr(torch.backends, "mps") and torch.backends.mps.is_available():
    print("Apple Silicon MPS available")
else:
    print("CPU only — inference will be slower but still works")

# Always set this for Ampere+ GPUs (A100, RTX 3090, etc.)
torch.set_float32_matmul_precision("high")

Batch Size Tuning

# Start conservative, increase until OOM
# GPU with 8 GB VRAM:  per_core_batch_size=64
# GPU with 16 GB VRAM: per_core_batch_size=128
# GPU with 24 GB VRAM: per_core_batch_size=256
# CPU with 8 GB RAM:   per_core_batch_size=8
# CPU with 16 GB RAM:  per_core_batch_size=32
# CPU with 32 GB RAM:  per_core_batch_size=64

model.compile(timesfm.ForecastConfig(
    max_context=1024,
    max_horizon=256,
    per_core_batch_size=32,  # <-- tune this
    normalize_inputs=True,
    use_continuous_quantile_head=True,
    fix_quantile_crossing=True,
))

Memory-Constrained Environments

import gc, torch

# Force garbage collection before loading
gc.collect()
if torch.cuda.is_available():
    torch.cuda.empty_cache()

# Load model
model = timesfm.TimesFM_2p5_200M_torch.from_pretrained(
    "google/timesfm-2.5-200m-pytorch"
)

# Use small batch size on low-memory machines
model.compile(timesfm.ForecastConfig(
    max_context=512,        # Reduce context if needed
    max_horizon=128,        # Reduce horizon if needed
    per_core_batch_size=4,  # Small batches
    normalize_inputs=True,
    use_continuous_quantile_head=True,
    fix_quantile_crossing=True,
))

# Process series in chunks to avoid OOM
CHUNK = 50
all_results = []
for i in range(0, len(inputs), CHUNK):
    chunk = inputs[i:i+CHUNK]
    p, q = model.forecast(horizon=H, inputs=chunk)
    all_results.append((p, q))
    gc.collect()  # Clean up between chunks

🔗 Integration with Other Skills

With `statsmodels`

Use statsmodels for classical models (ARIMA, SARIMAX) as a comparison baseline:

# TimesFM forecast
tfm_point, tfm_q = model.forecast(horizon=H, inputs=[values])

# statsmodels ARIMA forecast
from statsmodels.tsa.arima.model import ARIMA
arima = ARIMA(values, order=(1,1,1)).fit()
arima_forecast = arima.forecast(steps=H)

# Compare
print(f"TimesFM MAE: {np.mean(np.abs(actual - tfm_point[0])):.2f}")
print(f"ARIMA MAE:   {np.mean(np.abs(actual - arima_forecast)):.2f}")

With `matplotlib` / `scientific-visualization`

Plot forecasts with prediction intervals as publication-quality figures.

With `exploratory-data-analysis`

Run EDA on the time series before forecasting to understand trends, seasonality, and stationarity.

📚 Available Scripts

`scripts/check_system.py`

Mandatory preflight checker. Run before first model load.

python scripts/check_system.py

Output example:

=== TimesFM System Requirements Check ===

[RAM]       Total: 32.0 GB | Available: 24.3 GB  ✅ PASS
[GPU]       NVIDIA RTX 4090 | VRAM: 24.0 GB      ✅ PASS
[Disk]      Free: 142.5 GB                        ✅ PASS
[Python]    3.12.1                                 ✅ PASS
[timesfm]   Installed (2.5.0)                      ✅ PASS
[torch]     Installed (2.4.1+cu121)                ✅ PASS

VERDICT: ✅ System is ready for TimesFM 2.5 (GPU mode)
Recommended: per_core_batch_size=128

`scripts/forecast_csv.py`

End-to-end CSV forecasting with automatic system check.

python scripts/forecast_csv.py input.csv \
    --horizon 24 \
    --date-col date \
    --value-cols sales,revenue \
    --output forecasts.csv

📖 Reference Documentation

Detailed guides in references/:

File	Contents
`references/system_requirements.md`	Hardware tiers, GPU/CPU selection, memory estimation formulas
`references/api_reference.md`	Full `ForecastConfig` docs, `from_pretrained` options, output shapes
`references/data_preparation.md`	Input formats, NaN handling, CSV loading, covariate setup

Common Pitfalls

Not running system check → model load crashes on low-RAM machines. Always run check_system.py first.
Forgetting model.compile() → RuntimeError: Model is not compiled. Must call compile() before forecast().
Not setting normalize_inputs=True → unstable forecasts for series with large values.
Using v1/v2 on machines with < 32 GB RAM → use TimesFM 2.5 (200M params) instead.
Not setting fix_quantile_crossing=True → quantiles may not be monotonic (q10 > q50).
Huge per_core_batch_size on small GPU → CUDA OOM. Start small, increase.
Passing 2-D arrays → TimesFM expects a list of 1-D arrays, not a 2-D matrix.
Forgetting torch.set_float32_matmul_precision("high") → slower inference on Ampere+ GPUs.
Not handling NaN in output → edge cases with very short series. Always check np.isnan(point).any().
Using infer_is_positive=True for series that can be negative → clamps forecasts at zero. Set False for temperature, returns, etc.

Model Versions

timeline
    accTitle: TimesFM Version History
    accDescr: Timeline of TimesFM model releases showing parameter counts and key improvements.

    section 2024
        TimesFM 1.0 : 200M params, 2K context, JAX only
        TimesFM 2.0 : 500M params, 2K context, PyTorch + JAX
    section 2025
        TimesFM 2.5 : 200M params, 16K context, quantile head, no frequency indicator

Version	Params	Context	Quantile Head	Frequency Flag	Status
2.5	200M	16,384	✅ Continuous (30M)	❌ Removed	Latest
2.0	500M	2,048	✅ Fixed buckets	✅ Required	Archived
1.0	200M	2,048	✅ Fixed buckets	✅ Required	Archived

Hugging Face checkpoints:

google/timesfm-2.5-200m-pytorch (recommended)
google/timesfm-2.5-200m-flax
google/timesfm-2.0-500m-pytorch (archived)
google/timesfm-1.0-200m-pytorch (archived)

Resources

Paper: A Decoder-Only Foundation Model for Time-Series Forecasting (ICML 2024)
Repository: https://github.com/google-research/timesfm
Hugging Face: https://huggingface.co/collections/google/timesfm-release-66e4be5fdb56e960c1e482a6
Google Blog: https://research.google/blog/a-decoder-only-foundation-model-for-time-series-forecasting/
BigQuery Integration: https://cloud.google.com/bigquery/docs/timesfm-model

Examples

Three fully-working reference examples live in examples/. Use them as ground truth for correct API usage and expected output shape.

Example	Directory	What It Demonstrates	When To Use It
Global Temperature Forecast	`examples/global-temperature/`	Basic `model.forecast()` call, CSV -> PNG -> GIF pipeline, 36-month NOAA context	Starting point; copy-paste baseline for any univariate series
Anomaly Detection	`examples/anomaly-detection/`	Two-phase detection: linear detrend + Z-score on context, quantile PI on forecast; 2-panel viz	Any task requiring outlier detection on historical + forecasted data
Covariates (XReg)	`examples/covariates-forecasting/`	`forecast_with_covariates()` API (TimesFM 2.5), covariate decomposition, 2x2 shared-axis viz	Retail, energy, or any series with known exogenous drivers

Running the Examples

# Global temperature (no TimesFM 2.5 needed)
cd examples/global-temperature && python run_forecast.py && python visualize_forecast.py

# Anomaly detection (uses TimesFM 1.0)
cd examples/anomaly-detection && python detect_anomalies.py

# Covariates (API demo -- requires TimesFM 2.5 + timesfm[xreg] for real inference)
cd examples/covariates-forecasting && python demo_covariates.py

Expected Outputs

Example	Key output files	Acceptance criteria
global-temperature	`output/forecast_output.json`, `output/forecast_visualization.png`	`point_forecast` has 12 values; PNG shows context + forecast + PI bands
anomaly-detection	`output/anomaly_detection.json`, `output/anomaly_detection.png`	Sep 2023 flagged CRITICAL (z >= 3.0); >= 2 forecast CRITICAL from injected anomalies
covariates-forecasting	`output/sales_with_covariates.csv`, `output/covariates_data.png`	CSV has 108 rows (3 stores x 36 weeks); stores have distinct price arrays

Quality Checklist

Run this checklist after every TimesFM task before declaring success:

Common Mistakes

These bugs have appeared in this skill's examples. Learn from them:

Quantile index off-by-one -- The most common mistake. quant_fc[..., 0] is the mean, not q0. q10 = index 1, q90 = index 9. Always define named constants: IDX_Q10, IDX_Q20, IDX_Q80, IDX_Q90 = 1, 2, 8, 9.

Variable shadowing in comprehensions -- If you build per-series covariate dicts inside a loop, do NOT use the loop variable as the comprehension variable. Accumulate into separate dict[str, ndarray] outside the loop, then assign.

# WRONG -- outer `store_id` gets shadowed:
covariates = {store_id: arr[store_id] for store_id in stores}  # inside outer loop over store_id
# CORRECT -- use a different name or accumulate beforehand:
prices_by_store: dict[str, np.ndarray] = {}
for store_id, config in stores.items():
    prices_by_store[store_id] = compute_price(config)

Wrong CSV column name -- The global-temperature CSV uses anomaly_c, not anomaly. Always print(df.columns) before accessing.
tight_layout() warning with sharex=True -- Harmless; suppress with plt.tight_layout(rect=[0, 0, 1, 0.97]) or ignore.
TimesFM 2.5 required for forecast_with_covariates() -- TimesFM 1.0 does NOT have this method. Install pip install timesfm[xreg] and use checkpoint google/timesfm-2.5-200m-pytorch.
Future covariates must span the full horizon -- Dynamic covariates (price, promotions, holidays) must have values for BOTH the context AND the forecast horizon. You cannot pass context-only arrays.
Anomaly thresholds must be defined once -- Define CRITICAL_Z = 3.0, WARNING_Z = 2.0 as module-level constants. Never hardcode 3 or 2 inline.
Context anomaly detection uses residuals, not raw values -- Always detrend first (np.polyfit linear, or seasonal decomposition), then Z-score the residuals. Raw-value Z-scores are misleading on trending data.

Validation & Verification

Use the example outputs as regression baselines. If you change forecasting logic, verify:

# Anomaly detection regression check:
python -c "
import json
d = json.load(open('examples/anomaly-detection/output/anomaly_detection.json'))
ctx = d['context_summary']
assert ctx['critical'] >= 1, 'Sep 2023 must be CRITICAL'
assert any(r['date'] == '2023-09' and r['severity'] == 'CRITICAL'
           for r in d['context_detections']), 'Sep 2023 not found'
print('Anomaly detection regression: PASS')"

# Covariates regression check:
python -c "
import pandas as pd
df = pd.read_csv('examples/covariates-forecasting/output/sales_with_covariates.csv')
assert len(df) == 108, f'Expected 108 rows, got {len(df)}'
prices = df.groupby('store_id')['price'].mean()
assert prices['store_A'] > prices['store_B'] > prices['store_C'], 'Store price ordering wrong'
print('Covariates regression: PASS')"

When it activates

Automatically, when a task matches. Use for any univariate time series (sales, sensors, energy, vitals, weather) without training a custom model. Supports CSV/DataFrame/array inputs with point forecasts and prediction intervals. Includes a preflight system checker script to verify RAM/GPU before first use.

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skills/timesfm-forecasting/SKILL.md

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License Apache-2.0 licenseImported 8/1/2026Version v1Bundled 5 files · scripts

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TimesFM Forecasting

Overview

When to Use This Skill

⚠️ Mandatory Preflight: System Requirements Check

Hardware Requirements by Model Version

🔧 Installation

Step 1: Verify System (always first)

Step 2: Install TimesFM

Step 3: Install PyTorch for Your Hardware

Step 4: Verify Installation

🎯 Quick Start

Minimal Example (5 Lines)

Forecast from CSV

Forecast with Covariates (XReg)

Anomaly Detection (via Quantile Intervals)

📊 Understanding the Output

Quantile Forecast Structure

Extracting Prediction Intervals

🔧 ForecastConfig Reference

📋 Common Workflows

Workflow 1: Single Series Forecast

Workflow 2: Batch Forecasting (Many Series)

Workflow 3: Evaluate Forecast Accuracy

⚙️ Performance Tuning

GPU Acceleration

Batch Size Tuning

Memory-Constrained Environments

🔗 Integration with Other Skills

With statsmodels

With matplotlib / scientific-visualization

With exploratory-data-analysis

📚 Available Scripts

scripts/check_system.py

scripts/forecast_csv.py

📖 Reference Documentation

Common Pitfalls

Model Versions

Resources

Examples

Running the Examples

Expected Outputs

Quality Checklist

Common Mistakes

Validation & Verification

When it activates

SkillSafety

Source

Other skills for the same job

Related skills

More in the registry

With `statsmodels`

With `matplotlib` / `scientific-visualization`

With `exploratory-data-analysis`

`scripts/check_system.py`

`scripts/forecast_csv.py`