Lesson 260
Time Series Forecasting
Trend, Seasonality & ARIMA
1:00How to predict the next value in an ordered sequence by decomposing it into trend, seasonality, and noise, then applying baselines, ARIMA, and honest time-based evaluation.
By the end, you can
- Explain why temporal order is essential and why shuffling a time series corrupts a model.
- Decompose a series into trend, seasonality, and residual, and choose between additive and multiplicative forms.
- Apply first-order differencing and explain how it achieves stationarity.
- Define ARIMA and interpret its three order parameters (p, d, q).
- Compute a moving-average value by hand given a window size and a short series.
- Compute MAE from predictions and actuals, and explain when RMSE or MAPE is preferred.
- Describe the walk-forward backtest and identify at least two sources of data leakage in a time-series pipeline.
Up next in Advanced Algorithms, Math & PL Theory
