Work

The Usage of Lasso-Regression for Optimal Selection of Exogenous Variables for SARIMAX Models

Year: 2025

Type: article

Abstract: . Time series forecasting and data gap filling are significant tasks in applied science. Nowadays there are a lot of different useful methods for forecasting missing gaps in the data. However, it should be taken into account that the inclusion of a large number of features may lead to overfitting of the model and a decrease in the forecast quality. This paper examines the problem of forecast error minimization in time series with exogenous variables, in which the missing gaps are forecasted by the SARIMAX model. The analysis of Tver region data showed that the selection of significant exogenous variables using Lasso-regression allows for minimization of the forecast error and prevents model overfitting. The obtained results confirm that the correct choice of exogenous variables significantly improves the forecast quality.

Source: The international archives of the photogrammetry, remote sensing and spatial information sciences/International archives of the photogrammetry, remote sensing and spatial information sciences

DOI: 10.5194/isprs-archives-xlviii-2-w9-2025-39-2025

Authors: Anfisa I. Baliuk, Alexey I. Chulichkov

Institution: Lomonosov Moscow State University

Language: English

Topic: Forecasting Techniques and Applications

Subfield: Management Science and Operations Research

Field: Decision Sciences

Domain: Social Sciences

Open Access status: diamond

Funder: Lomonosov Moscow State University

OpenAlex ID: W4413977481