STA 364 Spring 2025

Outline

Outline

  1. The basic steps and concepts of forecasting. Examples: Closer forecasts will be better, and predictable patterns will be easy to forecast.

  2. Time series graphics

  • Time plots
  • Patterns, trend, season, cycle
  • Interpreting gg_season, gg_subseries graphs.
  • Autocorrelation, lag plots, correlogram
  • White noise
  • Connecting/cross-referencing information from these plots.
  1. Decomposition
  • Transformations and adjustments - multiplicative to additive, box_cox use.
  • TS components, Trend, season, remainder, seasonally adjusted series
  • Moving average
  • STL decomposition (that it uses loess at multiple stages)
  1. Toolbox
  • EDA -> Define model -> Train model -> Check performance -> produce forecasts
  • Train/ Test. Why. Using test sets to compare models but using metrics on either to evaluate an individual model.
  • Simple forecasting methods: MEAN, NAIVE, SNAIVE, DRIFT
    • When to use which, and how to model with them.
  • Residuals and residual diagnostic (check for autocorrelation)
  • Prediction intervals
  • Forecasting with decomposition
  1. TSLM
  • Linear model in TS
  • Regression model assessment. AIC, AICc, BIC, R2, and R2Adj
  • Picking useful predictors: dummy, harmonic, step, spike, lagged values
  • Nonlinear: piece-wise, log-log
This in-class exam is worth 100 points. The take-home is worth 100 points.