Counterfactual Explanation for Multivariate Time Series Forecasting with Exogenous Variables

TL;DR

This paper introduces a novel method for generating counterfactual explanations in multivariate time series forecasting with exogenous variables, enhancing interpretability and decision-making in machine learning models.

Contribution

It presents a new approach for extracting counterfactual explanations in time series forecasting, focusing on variable influence and practical applicability.

Findings

Method achieves high accuracy in generating explanations

Demonstrates effectiveness in real-world scenarios

Provides insights into variable influence over time

Abstract

Currently, machine learning is widely used across various domains, including time series data analysis. However, some machine learning models function as black boxes, making interpretability a critical concern. One approach to address this issue is counterfactual explanation (CE), which aims to provide insights into model predictions. This study focuses on the relatively underexplored problem of generating counterfactual explanations for time series forecasting. We propose a method for extracting CEs in time series forecasting using exogenous variables, which are frequently encountered in fields such as business and marketing. In addition, we present methods for analyzing the influence of each variable over an entire time series, generating CEs by altering only specific variables, and evaluating the quality of the resulting CEs. We validate the proposed method through theoretical analysis and empirical experiments, showcasing its accuracy and practical applicability. These contributions are expected to support real-world decision-making based on time series data analysis.