Editing AI for Time Series and Forecasting (section)

== <span style="color: #FFFFFF;">Remembering</span> ==
* '''Time series''' — A sequence of data points indexed in time order, typically at regular intervals.
* '''Forecasting''' — Predicting future values of a time series based on its historical patterns.
* '''Univariate time series''' — A single variable measured over time (e.g., daily sales).
* '''Multivariate time series''' — Multiple variables measured simultaneously over time (e.g., temperature, humidity, and pressure together).
* '''Trend''' — The long-term direction of a time series (upward, downward, or flat).
* '''Seasonality''' — Regular, periodic patterns that repeat at known intervals (daily, weekly, yearly).
* '''Residuals''' — The component remaining after removing trend and seasonality; ideally random noise.
* '''Stationarity''' — A time series is stationary if its statistical properties (mean, variance) do not change over time. Many models require stationarity.
* '''Autocorrelation''' — The correlation of a time series with its own past values (lags).
* '''Lag''' — A prior time step. Lag-1 is yesterday's value; lag-7 is last week's value.
* '''ARIMA''' — AutoRegressive Integrated Moving Average; a classical statistical model for univariate forecasting.
* '''LSTM (Long Short-Term Memory)''' — A type of RNN with gating mechanisms that captures long-range dependencies in sequences.
* '''Temporal Fusion Transformer (TFT)''' — A transformer-based model for multi-horizon time series forecasting, incorporating attention across time.
* '''Anomaly detection''' — Identifying data points, intervals, or patterns that deviate significantly from expected behavior.
* '''Horizon''' — The number of future time steps to forecast (1-step-ahead vs. multi-step/multi-horizon).
* '''Rolling forecast''' — Re-fitting or updating the model as new data arrives, maintaining accuracy over time.
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