Time-Series Forecasting
Predict future values from trends, seasonality, and lagged signals.
Time-series forecasting predicts future values of ordered sequences from trends, seasonality, and lagged signals. The methods below span classical statistical models, feature-based gradient boosting, and modern neural sequence models.
- For business forecasting, start with ETS/SARIMA.
- Then try gradient boosting with lag features, then deep learning if you have lots of data.
| # | Algorithm | Best for | Common fields |
|---|---|---|---|
| 1 | ARIMA / SARIMA | Classical univariate forecasting |
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| 2 | Exponential Smoothing / ETS / Holt-Winters | Trend and seasonality |
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| 3 | Prophet-style additive models | Business forecasting with seasonality/holidays |
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| 4 | Gradient Boosting for Time Series | Feature-based forecasting |
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| 5 | LSTM / GRU | Sequence-heavy forecasting |
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| 6 | Temporal CNN / TCN | Long sequence forecasting |
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| 7 | Transformer Forecasting Models | Large-scale multivariate forecasting |
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| 8 | State Space Models / Kalman Filter | Dynamic systems |
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