Improving out-of-sample forecasts of stock price indexes with forecast reconciliation and clustering
| Year of publication: |
2024
|
|---|---|
| Authors: | Mattera, Raffaele ; Athanasopoulos, George ; Hyndman, Rob J. |
| Published in: |
Quantitative finance. - London : Taylor & Francis, ISSN 1469-7696, ZDB-ID 2027557-2. - Vol. 24.2024, 11, p. 1641-1667
|
| Subject: | Clustering | Finance | Financial time series | Hierarchical forecasting | Machine learning | Prediction | Unsupervised learning | Prognoseverfahren | Forecasting model | Künstliche Intelligenz | Artificial intelligence | Zeitreihenanalyse | Time series analysis | Börsenkurs | Share price | Prognose | Forecast | Aktienindex | Stock index | Clusteranalyse | Cluster analysis | Theorie | Theory | Lernprozess | Learning process | Regionales Cluster | Regional cluster |
-
Improving out-of-sample forecasts of stock price indexes with forecast reconciliation and clustering
Mattera, Raffaele, (2023)
-
Jointly modeling and clustering tensors in high dimensions
Cai, Biao, (2025)
-
Asset return prediction via machine learning
Zhang, Liangliang, (2019)
- More ...
-
Improving out-of-sample forecasts of stock price indexes with forecast reconciliation and clustering
Mattera, Raffaele, (2023)
-
Meta-learning how to forecast time series
Talagala, Thiyanga S., (2018)
-
Probabilisitic forecasts in hierarchical time series
Gamakumara, Puwasala, (2018)
- More ...