the breaks in the parameters and in the covariance of a system of long memory time series regressions is proposed. The …This paper focuses on the estimation and testing of multiple breaks that occur at unknown dates in multivariate long … memory time series regression models, allowing for fractional cointegration. A likelihood-ratio based approach for estimating …

the breaks in the parameters and in the covariance of a system of long memory time series regressions is proposed. The …This paper focuses on the estimation and testing of multiple breaks that occur at unknown dates in multivariate long … memory time series regression models, allowing for fractional cointegration. A likelihood-ratio based approach for estimating …

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 …

the structural breaks by means of the Bai and Perron contrast, together with estimates of long memory using the 2ELW …

the estimation of the long memory through the 2ELW estimator. Additionally, the influence of exogenous variables that …

characterised by long memory (a well-known property of most climatological series) – in this case, the time trend becomes …