Time Series Data Generation by Linear Response Model An Application of Linear Response Theory to Finance

In recent years, data generation techniques based on machine learning approaches have made significant progress and various generation models have been proposed in the fields of text and image. In the field of finance, there has also been research on the data generation of time series on stock prices. However, there are many problems in applying machine learning models to financial time series data. For example, the behavior of the machine learning model when given data that is not in the historical data is unclear, or if the data differs significantly from historical data under strong stresses such as the Lehman or the COVID-19 shock, the predictions by the machine learning model will be completely unreliable. This problem may be solvable by using data generation techniques that add virtual stress to the current state. In this study, we propose a new method for generating virtual time series data based on linear response theory (LRT). Using the LRT, we can obtain an approximate representation of the transition from the current equilibrium state to another equilibrium state by adding second-order fluctuation in the current equilibrium state and external forces (stresses). In other words, if the external force can be estimated in advance, we shall be able to obtain virtual time series data under external forces. As an application, we examined whether this method is effective with data augmentation of the stock prediction model using external forces estimated by historical data. As a result, the accuracy of stock price predictions was improved over the case without data augmentation. © 2024, Japanese Society for Artificial Intelligence. All rights reserved.