Section 7 of 2
Data preparation, forecasting models, deep learning, foundation models, evaluation, maintenance.
8 chapters in this section.
A practical guide to the main types of time series data and the common confusion between multivariate, panel, and exogenous-variable forecasting.
A hands-on explanation of how raw time series data is cleaned, regularized, interpolated, bucketed, and prepared for reliable modeling or visualization.
An introduction to statistical baseline methods for time-series anomaly detection: Z-Score, IQR, moving averages, simple ensembles, and evaluation metrics.
A practical guide to ARIMA, SARIMA, and SARIMAX: how classical statistical forecasting models handle autocorrelation, differencing, seasonality, external regressors, diagnostics, and where they still fit in modern forecasting.
An overview of deep learning architectures for time-series forecasting: LSTM, TCN, DeepAR, N-BEATS, and TFT, with a focus on input shapes, local vs global training, covariates, probabilistic outputs, and practical model selection.
How recurrent networks model sequences: vanilla RNN, the vanishing-gradient problem, the LSTM gating mechanism, cell state, and the practical use of return_sequences in Keras.
A practical explanation of Temporal Convolutional Networks for time-series forecasting: causal convolutions, dilations, receptive field, residual blocks, optional GLU gating, output heads, and when TCN is useful as a standalone or hybrid forecasting component.
Playing with a hybrid forecasting architecture, assembled out of common neural building blocks: TCN front-end, LSTM recurrence, multi-head attention, TFT-style VSN feature routing with GRN, N-BEATS-style decomposition heads, seasonal and event branches.