Editing AI for Time Series and Forecasting (section)

== <span style="color: #FFFFFF;">Understanding</span> ==
Time series forecasting is inherently a sequential problem: the order of observations matters, and the past contains information about the future. This distinguishes it from tabular classification, where rows are exchangeable.

'''The decomposition framework''' is key to understanding time series:
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Observed = Trend × Seasonal × Residual  (multiplicative)
         = Trend + Seasonal + Residual   (additive)
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Decomposing a series into these components enables targeted modeling: model the trend with regression, the seasonality with Fourier features or indicator variables, and the residual with a neural network or ARIMA.

'''Why deep learning?''' Classical models like ARIMA excel at capturing simple autocorrelation but struggle with:
* Non-linear relationships between variables
* Multiple interacting series (multivariate)
* Complex, multi-scale seasonality
* Incorporating exogenous variables (weather, holidays, promotions)

LSTMs can capture non-linear temporal dependencies and handle arbitrary-length sequences. Transformers add the ability to attend to any past time step directly, avoiding the vanishing gradient problem over long sequences. Foundation models for time series (TimeGPT, MOIRAI, Chronos) pre-trained on billions of time points can zero-shot forecast on new series.

'''Evaluation discipline''': A critical mistake in time series is using random train/test splits. This causes data leakage — future data leaks into the training set. Always use chronological splits: train on the first 70–80%, validate on the next 10–15%, test on the most recent 10–15%.
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