Statistical forecasting, ARIMA/SARIMA models, temporal feature engineering, recurrent deep learning (RNN/LSTM/GRU), and production forecasting systems.
A detailed foundation for understanding, exploring, visualizing, and preparing time series data before statistical, machine learning, and deep learning forecasting.
A practical and mathematical introduction to classical statistical forecasting models, from white noise and autoregression through ARIMA and SARIMA, with model selection and residual diagnostics.
Learn how to transform time series into supervised learning problems, engineer leakage-safe temporal features, train tree-based models, handle multiple seasonalities, and compare machine learning forecasting with classical statistical approaches.
A detailed practical and conceptual guide to sequence-based deep learning for time series, covering RNNs, LSTMs, GRUs, sequence windows, 3D tensors, training, forecasting, evaluation, and common pitfalls.
An advanced guide to modern time-series forecasting architectures, rigorous evaluation, multi-step forecasting, production retraining, monitoring, and model selection.