Forecasting Oil Prices with Time Series & Generalized Additive Models for Location, Scale and Shape
-
Updated
Jun 17, 2022 - HTML
Forecasting Oil Prices with Time Series & Generalized Additive Models for Location, Scale and Shape
This study utilised a 20-year dataset of seal counts from the Solent region of southeast England. Generalised additive models (GAMs) were used to examine yearly and seasonal trends in seal counts. This repository contains the raw data and code for analysis.
Ecological forecasting using Dynamic Generalized Additive Models with R 📦's {mvgam} and {brms}
Add a description, image, and links to the generalised-additive-models topic page so that developers can more easily learn about it.
To associate your repository with the generalised-additive-models topic, visit your repo's landing page and select "manage topics."