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Darts — time series forecasting library by Unit8. Unified API across ARIMA, Prophet, CatBoost, N-BEATS, TFT, TCN, Transformer, and RNN models. Backtesting, probabilistic forecasting, and covariate support.
Passed 0 of 23 test cases with the skill, and 23 of 23 without it.
0 fixed19 passed either way3 still failing1 broke
Tokens, turns and time describe resource use, not a dollar price.
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Darts (Unit8) provides a unified forecasting API across statistical models (ARIMA, Prophet, Theta), deep learning (N-BEATS, TFT, TCN, Transformer, RNN), and ensemble methods. Supports univariate/multivariate, probabilistic forecasting, covariate handling, and backtesting.
uv pip install darts
from darts import TimeSeries
from darts.models import ExponentialSmoothing
import pandas as pd
series = TimeSeries.from_dataframe(pd.DataFrame({"y": [1,2,3,4,5,6,7,8,9,10]}), value_cols="y")
model = ExponentialSmoothing()
model.fit(series)
forecast = model.predict(6)
print(forecast.values())
from darts.models import NBEATSModel
model = NBEATSModel(input_chunk_length=24, output_chunk_length=12)
model.fit(train, epochs=100)
pred = model.predict(12)
from darts.metrics import mae, mape
errors = model.backtest(series, start=0.7, forecast_horizon=6, stride=1)
print(f"MAE: {mae(errors):.3f}, MAPE: {mape(errors):.3f}")
test cases passed, vs 23 / 23 without
More skills from the same source and nearby in the registry, with their headline benchmark lift.