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- Title
Interactions in Forecasting.
- Authors
KOLASSA, STEPHAN
- Abstract
The article titled "Interactions in Forecasting" discusses the importance of considering interactions between predictors in forecasting models. The author explains that in forecasting, predictors can be time series-like or causal drivers, such as promotions or prices. The article highlights that interactions between predictors can have a significant impact on forecasting accuracy and that these interactions need to be explicitly modeled in regression models. The author also mentions that machine learning methods can automatically account for interactions. However, modeling interactions requires more data and can be more challenging to explain and communicate. The article provides examples and visualizations to illustrate the concept of interactions in forecasting.
- Subjects
BUSINESS schools; REGRESSION analysis
- Publication
Foresight: The International Journal of Applied Forecasting, 2024, Issue 73, p63
- ISSN
1555-9068
- Publication type
Article