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

Accurately detecting anomalous points in time-series data is critical, as false positives can mislead business … theoretically and empirically on time-series datasets. Our findings demonstrate that the Ensemble Bayesian-aided approach …

This paper studies linear time‐series regressions with many regressors. Weak exogeneity is the most used identifying … time‐series regressions with many controls may produce substantial biases and render the least squares (OLS) estimator … assumption in time series. Weak exogeneity requires the structural error to have zero conditional expectation given present and …

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. …

-related crimes in Mongolia using a time-series econometric approach. Relying on the theoretical frameworks of Becker’s economic model …

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 …

channels, this paper builds a novel climate economy model using nonlinear model predictive control. Moreover, using time-series …

in capturing nonlinear relationships and complex interactions in financial time series, particularly when using features …