Long Memory Time Series Analysis is a comprehensive text which covers long memory time series, with the different long memory time series discussed. The authors cover modelling and forecasting using various time series, deploying traditional and machine learning methodologies. The reader also learns recent research trends such as state space modelling of generalized long memory time series and the use of tsfGRNN machine learning tool in R. The book starts from autoregressive (AR) and moving average (MA) processes to the description of autoregressive integrated moving average (ARMA) time series, the ARIMA model, and the autoregressive fractionally integrated moving average (ARFIMA) process. The differences of short, intermediate, and long memory processes are highlighted. The reader will gain good knowledge of elementary time series through this extensive coverage. The book discusses generalized Gegenbauer autoregressive moving average (GARMA) and seasonal GARMA long memory time series, and state space modelling of generalized and seasonal GARMA. The extensions of the short and long memory models driven by generalised autoregressive conditionally heteroskedastic (GARCH) errors are also presented.

The extensive range of problems linked with generalized Gegenbauer long memory time series are presented in the book to help reinforce the reader's conceptual learning. The coverage on the use of time series with high frequency data captured through the latest technological innovations, is an invaluable resource to the reader. This learning is done through the examples of time series application case studies in the subject domains of medicine, biology, and finance. The core audience is the students attending advance studies in Time Series. It can also be used by researchers and data scientists involved in utilizing time series analysis in a modern context.