Editing AI for Time Series and Forecasting (section)

== <span style="color: #FFFFFF;">Analyzing</span> ==
{| class="wikitable"
|+ Time Series Model Comparison
! Model !! Type !! Strengths !! Weaknesses
|-
| ARIMA/SARIMA || Statistical || Interpretable, fast, works on small data || Assumes linearity, one series at a time
|-
| Prophet || Statistical || Handles holidays, trend changepoints || Limited to single series; no covariates
|-
| DeepAR || Deep Learning (LSTM) || Probabilistic, many series || Needs lots of data, slow training
|-
| TFT || Transformer || Multi-horizon, covariate-rich, interpretable || Complex, high data requirement
|-
| N-BEATS || Deep Learning (MLP) || Fast, competitive, no feature engineering || Limited covariate support
|-
| Chronos (foundation) || LLM-style || Zero-shot, no training needed || No covariate support yet; large model
|}

'''Failure modes:'''
* '''Chronological leakage''' — Random train/test splits allow future data to inform past predictions, producing falsely optimistic results. Always split chronologically.
* '''Ignoring non-stationarity''' — Many models assume stationarity. Differencing (ARIMA) or normalization per-series is required.
* '''Ignoring distributional shift''' — Retail models trained pre-COVID performed terribly during COVID. Extreme events cause structural breaks that no model trained on historical data anticipates.
* '''Point forecast overconfidence''' — Reporting only mean forecasts without uncertainty intervals. Downstream planning needs to understand the range of outcomes, not just the median.
* '''Evaluation on last segment only''' — Evaluating only on the final test period may not represent the model's general quality. Use rolling window backtesting across multiple historical windows.
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