Forecasting Multivariate Time Series with the Theta Method

By Dimitrios D. Thomakos, Konstantinos Nikolopoulos. 2015

23 citations recorded by the source corpus.

Subfields: Management Science and Operations Research, Statistics and Probability

How researchers see this paper

The researchers whose trusted networks rank it highest, strongest advocate first. Every researcher gets a different order.

In Management Science and Operations Research

1. Evangelos Spiliotis
2. Konstantinos Nikolopoulos
3. Alexei Botchkarev
4. Vassilios Assimakopoulos
5. Fotios Petropoulos
6. Spyros Makridakis
7. Rob J Hyndman
8. Slawek Smyl

In Statistics and Probability

1. Vassilios Assimakopoulos
2. Evangelos Spiliotis
3. Alexei Botchkarev
4. Konstantinos Nikolopoulos
5. Slawek Smyl

Citations in the corpus

• Forecasting with a hybrid method utilizing data smoothing, a variation of the Theta method and shrinkage of seasonal factors (2018, methodology): “For example, this could be done by exploiting the bivariate version of the Theta method (Thomakos and Nikolopoulos, 2015) but applied first on smoothed data rather than the original one: if the data are seasonal one can also experiment by damping the seasonal factors after the bivariate extrapolatio”
• Performance Metrics (Error Measures) in Machine Learning Regression, Forecasting and Prognostics: Properties and Typology (2018, methodology): “- Relative Root Mean Squared Error: RelRMSE = RMSE/RMSE b , where RMSE b is RMSE from a benchmark method, e.g. Chen, Twycross and Garibaldi (2017), Thomakos and Nikolopoulos (2015).”