Improved LSTM hyperparameters alongside sentiment walk-forward validation for time series prediction

Improved LSTM hyperparameters alongside sentiment walk-forward validation for time series prediction

This study aims to address the common issue of biased estimation errors in time series modeling by analyzing the error in locating ideal hyperparameters and defining appropriate validation methods. Specifically, it focuses on predicting the stock price of Bank Rakyat Indonesia using a combination of...

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Bibliographic Details
Main Authors: Eko Putra Wahyuddin, Rezzy Eko Caraka, Robert Kurniawan, Wahyu Caesarendra, Prana Ugiana Gio, Bens Pardamean
Format: Article
Language:English
Published: Elsevier 2025-03-01
Series:Journal of Open Innovation: Technology, Market and Complexity
Subjects:
Time series modeling
LSTM
Stock price prediction
Validation methods
Sentiment analysis
Online Access:http://www.sciencedirect.com/science/article/pii/S219985312400252X
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