Abstract
We apply a pattern matching algorithm to multidimensional forecasting. The algorithm searches for occurrences of patterns in multidimensional time series and computes their predictive accuracy. A genetic algorithm breeds then patterns that maximize this accuracy evolving ever better predictors. In an application to financial data, we show that the most successful patterns in training samples can retain a statistically and economically significant predictive power out-of-sample.
| Original language | English |
|---|---|
| Title of host publication | Proceedings ITISE 2014 |
| Subtitle of host publication | International work-conference on Time Series |
| Pages | 1161-1169 |
| Number of pages | 9 |
| ISBN (Electronic) | 978-84-15814-97-9, Ignacio Rojas Ruiz, Gonzalo Ruiz Garcia |
| Publication status | Published - Jun 2014 |
Keywords
- multidimensional forecasting
- genetic algorithms
- pattern matching