This paper examines the use of machine learning methods in modeling and forecasting time series with long memory …-LSTM model outperforms traditional approaches in forecasting long-memory time series. This characteristic is confirmed using … accurate and reliable forecasting in long-memory time series. Additionally, we compare the GARMA-LSTM model with other …

Applications of high-frequency data, including energy management, economics, and finance, frequently require time-series … for time-series analysis, and inform professionals seeking superior forecasting solutions in various fields. …

This study aims to address the common issue of biased estimation errors in time series modeling by analyzing the error …-based Long-Short Term Memory (LSTM) model, the study optimizes hyperparameters alongside walk-forward validation for time series … addressing the issue of biased estimation errors in time series modeling, offering insights into the optimization of …

investment strategy based on signal indicators derived from news indices focusing on short-term trends using time-series …