Time Series & Forecasting
Predicting what comes next from data over time: trend, seasonality, ARIMA, Prophet and deep-learning forecasters.
Beginner
Start here. No background needed beyond school maths.
Advanced
Once the basics are comfortable.
Darts: Quickstart
One Python library for many forecasting models, from ARIMA to deep learning, with backtesting, probabilistic forecasts and anomaly detection on the same time-series objects.
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StatsForecast Documentation
Fast statistical forecasting in Python: ARIMA, ETS, Theta and other models that run across thousands of series at once, with cross-validation and benchmarks.
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statsmodels: Time Series Analysis
Reference and examples for classical time-series models in Python: ARIMA and SARIMAX, exponential smoothing, VAR, state-space models and tests for stationarity.
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TensorFlow: Time Series Forecasting
A hands-on tutorial that forecasts weather data with windowed inputs, then compares dense, convolutional and recurrent (LSTM) neural networks against simple baselines.
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Expert
Research-level depth and specialist material.