Time Series Forecasting

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Predicting future values based on historical temporal data. Traditional methods (ARIMA) are being augmented by deep learning (LSTMs, Transformers). Critical for finance, weather, and demand planning. Stationarity, whether the series' statistics drift over time, decides which methods are even valid.

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Key terms

ARIMASeasonalityTrendLag featuresProphetStationarity

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