DAT320 Multivariate Forecasting with ARIMA (Autumn 2024) PDF
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Norwegian University of Life Sciences
2024
Norwegian University of Life Sciences
Kristian Hovde Liland
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Summary
These lecture notes cover multivariate extensions of ARIMA models for forecasting, focusing on dynamic regression, and providing examples of forecasting techniques using the airquality dataset. The session covers the concepts of ARIMA, Granger causality, and the distributed lag model (DLM).
Full Transcript
DAT320: Forecasting Multivariate extensions of ARIMA Kristian Hovde Liland [email protected] Autumn 2024 Norwegian University of Life Sciences Dynamic regression Models with lagged predictors Multivariate ARIMA Granger causality 1...
DAT320: Forecasting Multivariate extensions of ARIMA Kristian Hovde Liland [email protected] Autumn 2024 Norwegian University of Life Sciences Dynamic regression Models with lagged predictors Multivariate ARIMA Granger causality 1 Norwegian University of Life Sciences The airquality dataset Figure 1: Dataset "airquality" 2 Norwegian University of Life Sciences The airquality dataset library ( dplyr ) library ( lubridate ) library ( imputeTS ) library ( datasets ) library ( ggplot2 ) data ( " airquality " ) airquality % na _ ma () % >% dplyr :: select ( Ozone , Wind , Temp ) autoplot ( ts ( airquality ) ) + facet _ grid ( series ~. , scales = " free " ) airquality _ train
