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 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.