A Study of Multi-Constraint Planning Problem for Time Series Data with Correlation

This paper studies the multi-constraint optimization problem under the background of time series data correlation, which is particularly important in the fields of finance, energy and meteorology. Aiming at the multiple constraints that need to be considered at the same time in practical applications, such as time, resources and risks, this paper proposes an optimization algorithm that combines time series correlation analysis. Through theoretical analysis and model design, the algorithm has been verified to be effective in multiple application scenarios, especially in improving financial forecasting and decision-making efficiency. Then, we use the financial market time series data into the algorithm of this paper for optimization experiments. The experimental results show that our new algorithm can not only improve the accuracy of similar financial market prediction models, but also significantly improve the decision-making efficiency. Finally, this paper looks forward to the improvement of the method in complex constraints and the direction of more efficient algorithm, which provides ideas for further research.