Time Series Foundations
4concepts
28flashcards
28minutes of reading
- 01 Seasonality and Decomposition Splitting a series into trend, seasonal and remainder components, and the choice between additive and multiplicative structure that determines whether the seasonal pattern grows with the level.
- 02 Autocorrelation and ARIMA Reading the autocorrelation and partial autocorrelation functions to identify how much of a series is explained by its own past, and what the AR, I and MA components each represent.
- 03 Backtesting and Temporal Validation Random cross-validation on time series lets a model learn from the future, and the alternatives all trade honesty against how much of the data can be used.
- 04 Stationarity and Differencing Almost every time series method assumes the statistical properties do not change over time, and the transformations that enforce that assumption also change what the model is predicting.