Multi-Step-Ahead Prediction of Chaotic Time Series: Self-Healing Algorithm for Restoring Values at Non-Predictable Points
Problem: The study proposes a new algorithm for multi-step-ahead prediction of chaotic time series within the framework of the clustering-based forecasting paradigm. The introduction of the concept of non-predictable points enabled to avoid the exponential growth of the prediction error as a functi...
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| Format: | Article |
| Language: | English |
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Qubahan
2024-09-01
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| Series: | Qubahan Academic Journal |
| Online Access: | https://journal.qubahan.com/index.php/qaj/article/view/912 |
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| author | Vasilii A. Gromov Korney K. Tomashchuk Alexey Rukavishnikov A. |
| author_facet | Vasilii A. Gromov Korney K. Tomashchuk Alexey Rukavishnikov A. |
| author_sort | Vasilii A. Gromov |
| collection | DOAJ |
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Problem: The study proposes a new algorithm for multi-step-ahead prediction of chaotic time series within the framework of the clustering-based forecasting paradigm. The introduction of the concept of non-predictable points enabled to avoid the exponential growth of the prediction error as a function of the number of steps ahead, which made it possible to develop algorithms that predict many Lyapunov times (and many steps) ahead – the price for this turned out to be that some points remained non-predictable. In this work, we say that it is necessary to be of great help regarding the exemption and development of the scientific university. Method: This study proposes a self-healing algorithm, which is an iterative algorithm that takes as input the forecasts produced by the underlying prediction algorithm. At each iteration, the self-healing algorithm finds new possible predicted values, updates the status of the points from predictable to non-predictable or vice versa, and calculates new single predicted values for the predictable points. The study proposes several new algorithms for calculating a single prediction value and algorithms for determining non-predictable points. We have studied new publications of parameter estimates, importance monitoring, and predictive features with more views and size objectives. Results: In the results of our work, the new tools in specific indicators: RMSE was increased from 0.11 to 0.06, and MAPE was reduced from 0.38 to 0.04. Conclusion: Research has been conducted on the selection of parameters for the self-healing algorithm, its assessment, and the prediction quality compared to the existing prediction algorithm.
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| format | Article |
| id | doaj-art-f44bb6ca120e44eab9cf571143b1a859 |
| institution | Kabale University |
| issn | 2709-8206 |
| language | English |
| publishDate | 2024-09-01 |
| publisher | Qubahan |
| record_format | Article |
| series | Qubahan Academic Journal |
| spelling | doaj-art-f44bb6ca120e44eab9cf571143b1a8592025-02-03T10:11:34ZengQubahanQubahan Academic Journal2709-82062024-09-014310.48161/qaj.v4n3a912912Multi-Step-Ahead Prediction of Chaotic Time Series: Self-Healing Algorithm for Restoring Values at Non-Predictable PointsVasilii A. Gromov0Korney K. Tomashchuk1Alexey Rukavishnikov A.2International Laboratory for Intelligent Systems and Structural Analysis, Faculty of Computer Science, HSE University, Moscow, Russia;International Laboratory for Intelligent Systems and Structural Analysis, Faculty of Computer Science, HSE University, Moscow, Russia;School of Data Analysis and Artificial Intelligence, Faculty of Computer Science, HSE University, Moscow, Russia. Problem: The study proposes a new algorithm for multi-step-ahead prediction of chaotic time series within the framework of the clustering-based forecasting paradigm. The introduction of the concept of non-predictable points enabled to avoid the exponential growth of the prediction error as a function of the number of steps ahead, which made it possible to develop algorithms that predict many Lyapunov times (and many steps) ahead – the price for this turned out to be that some points remained non-predictable. In this work, we say that it is necessary to be of great help regarding the exemption and development of the scientific university. Method: This study proposes a self-healing algorithm, which is an iterative algorithm that takes as input the forecasts produced by the underlying prediction algorithm. At each iteration, the self-healing algorithm finds new possible predicted values, updates the status of the points from predictable to non-predictable or vice versa, and calculates new single predicted values for the predictable points. The study proposes several new algorithms for calculating a single prediction value and algorithms for determining non-predictable points. We have studied new publications of parameter estimates, importance monitoring, and predictive features with more views and size objectives. Results: In the results of our work, the new tools in specific indicators: RMSE was increased from 0.11 to 0.06, and MAPE was reduced from 0.38 to 0.04. Conclusion: Research has been conducted on the selection of parameters for the self-healing algorithm, its assessment, and the prediction quality compared to the existing prediction algorithm. https://journal.qubahan.com/index.php/qaj/article/view/912 |
| spellingShingle | Vasilii A. Gromov Korney K. Tomashchuk Alexey Rukavishnikov A. Multi-Step-Ahead Prediction of Chaotic Time Series: Self-Healing Algorithm for Restoring Values at Non-Predictable Points Qubahan Academic Journal |
| title | Multi-Step-Ahead Prediction of Chaotic Time Series: Self-Healing Algorithm for Restoring Values at Non-Predictable Points |
| title_full | Multi-Step-Ahead Prediction of Chaotic Time Series: Self-Healing Algorithm for Restoring Values at Non-Predictable Points |
| title_fullStr | Multi-Step-Ahead Prediction of Chaotic Time Series: Self-Healing Algorithm for Restoring Values at Non-Predictable Points |
| title_full_unstemmed | Multi-Step-Ahead Prediction of Chaotic Time Series: Self-Healing Algorithm for Restoring Values at Non-Predictable Points |
| title_short | Multi-Step-Ahead Prediction of Chaotic Time Series: Self-Healing Algorithm for Restoring Values at Non-Predictable Points |
| title_sort | multi step ahead prediction of chaotic time series self healing algorithm for restoring values at non predictable points |
| url | https://journal.qubahan.com/index.php/qaj/article/view/912 |
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