Using cross-validation methods to select time series models: promises and pitfalls
From MaRDI portal
Publication:6559931
DOI10.1111/BMSP.12330zbMATH Open1540.62207MaRDI QIDQ6559931
Publication date: 21 June 2024
Published in: British Journal of Mathematical \& Statistical Psychology (Search for Journal in Brave)
Time series, auto-correlation, regression, etc. in statistics (GARCH) (62M10) Applications of statistics to psychology (62P15)
Cites Work
- Title not available (Why is that?)
- Title not available (Why is that?)
- Title not available (Why is that?)
- Title not available (Why is that?)
- Penalized Estimation and Forecasting of Multiple Subject Intensive Longitudinal Data
- A new method of the description of the information flow in the brain structures
- To explain or to predict?
- Estimating the dimension of a model
- A systematic study into the factors that affect the predictive accuracy of multilevel VAR(1) models
- The Predictive Sample Reuse Method with Applications
- Person‐specific versus multilevel autoregressive models: Accuracy in parameter estimates at the population and individual levels
- An Introduction to Statistical Learning
- An investigation of model selection criteria for neural network time series forecasting
- A new look at the statistical model identification
- The Elements of Statistical Learning
- Cross-Validation: What Does It Estimate and How Well Does It Do It?
This page was built for publication: Using cross-validation methods to select time series models: promises and pitfalls
Report a bug (only for logged in users!)Click here to report a bug for this page (MaRDI item Q6559931)