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2024 | OriginalPaper | Chapter

Multiple Time Series Analysis with LSTM

Authors : Hasan Şen, Ömer Faruk Efe

Published in: Advances in Intelligent Manufacturing and Service System Informatics

Publisher: Springer Nature Singapore

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Abstract

The chapter 'Multiple Time Series Analysis with LSTM' delves into the intricate process of forecasting inflation using Long Short-Term Memory (LSTM) neural networks. It begins by defining inflation and its economic implications, particularly focusing on its impact on economic growth and the purchasing power of money. The study examines the effects of various parameters such as gold prices, dollar rates, oil prices, exports, unemployment, and housing price indices on inflation. The methodology involves preparing and normalizing data, applying the Augmented Dickey-Fuller (ADF) test for stationarity, and creating an LSTM model with optimized hyperparameters. The results, evaluated through Mean Absolute Percentage Error (MAPE) and Mean Squared Error (MSE), demonstrate the LSTM model's effectiveness in inflation forecasting. This chapter offers valuable insights into the application of advanced machine learning techniques for economic forecasting, making it a compelling read for professionals interested in the intersection of economics and data science.

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Metadata
Title
Multiple Time Series Analysis with LSTM
Authors
Hasan Şen
Ömer Faruk Efe
Copyright Year
2024
Publisher
Springer Nature Singapore
DOI
https://doi.org/10.1007/978-981-99-6062-0_72

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