Advances in Economics, Management and Political Sciences

- The Open Access Proceedings Series for Conferences


Proceedings of the 2nd International Conference on Financial Technology and Business Analysis

Series Vol. 73 , 05 March 2024


Open Access | Article

Inflation Forecasting Using a Hybrid LSTM-SARIMA Model Based on Discrete Wavelet Transform

Shizhe Li * 1
1 Department of economics, Union College, NY, U.S. 12308

* Author to whom correspondence should be addressed.

Advances in Economics, Management and Political Sciences, Vol. 73, 100-108
Published 05 March 2024. © 2023 The Author(s). Published by EWA Publishing
This article is an open access article distributed under the terms and conditions of the Creative Commons Attribution (CC BY) license, which permits unrestricted use, distribution, and reproduction in any medium, provided the original work is properly cited.
Citation Shizhe Li. Inflation Forecasting Using a Hybrid LSTM-SARIMA Model Based on Discrete Wavelet Transform. AEMPS (2024) Vol. 73: 100-108. DOI: 10.54254/2754-1169/73/20231419.

Abstract

The accurate prediction of inflation is of utmost importance in informing economic policy formulation and guiding private investment choices. Enhancing the precision of inflation prediction through the utilization of univariate models continues to present difficulties. Inspired by the hybrid methology that has emerged in the field of time series forecasting, which seeks to enhance forecasting accuracy by integrating linear and nonlinear approaches, the author employs a hybrid model that combines Long Short-Term Memory (LSTM) and Seasonal Autoregressive Integrated Moving Average (SARIMA) techniques. The objective is to forecast the non-seasonally adjusted monthly US Consumer Price Index (CPI) inflation. The hybrid model employed in this study incorporates the utilization of wavelet transform, namely the discrete wavelet transforms (DWT), for the purpose of breaking the time series data into its constituent components of details and approximation. The subsequent application of LSTM and SARIMA models aims to capture both nonlinear and linear patterns present in the data. The findings of this research indicate that the DWT-based LSTM-SARIMA model outperforms both the solo SARIMA and LSTM models in a one-step rolling window forecast over a period of five years. This superiority is particularly evident during periods characterized by severe levels of inflation.

Keywords

Inflation rate, Long Short-Term Memory (LSTM), Autoregressive Integrated Moving Average (ARIMA), DWT

References

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Data Availability

The datasets used and/or analyzed during the current study will be available from the authors upon reasonable request.

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Volume Title
Proceedings of the 2nd International Conference on Financial Technology and Business Analysis
ISBN (Print)
978-1-83558-319-7
ISBN (Online)
978-1-83558-320-3
Published Date
05 March 2024
Series
Advances in Economics, Management and Political Sciences
ISSN (Print)
2754-1169
ISSN (Online)
2754-1177
DOI
10.54254/2754-1169/73/20231419
Copyright
© 2023 The Author(s)
Open Access
This article is an open access article distributed under the terms and conditions of the Creative Commons Attribution (CC BY) license, which permits unrestricted use, distribution, and reproduction in any medium, provided the original work is properly cited

Copyright © 2023 EWA Publishing. Unless Otherwise Stated