darts
SkillAI & modelsDarts — 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.
Available today. Use it from your connected AI after setup.
No other account needed.
Connect ahel once, and every AI you use reads what you have installed.
Then ask your AI: use the darts skill
What this skill tells your AI
The instructions your AI receives, as published by mkurman/zorai in skills/scientific-skills/darts/SKILL.md and read by ahel’s review.
Overview
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.
Installation
uv pip install darts
Basic Forecast
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())
Deep Learning (N-BEATS)
from darts.models import NBEATSModel
model = NBEATSModel(input_chunk_length=24, output_chunk_length=12)
model.fit(train, epochs=100)
pred = model.predict(12)
Backtesting
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}")
References
Signals
- GitHub stars
- 324
- Forks
- 26
- Last commit
- Sep 2026
Advanced
- Catalog kind
- skill
- Gateway key
darts- Source
- github.com/mkurman/zorai