Time series data affect many aspects of our lives. This paper highlights ten things we should all know about time series, namely: a good working knowledge of econometrics and statistics, an awareness of measurement errors, testing for zero frequency, seasonal and periodic unit roots, analysing...

A new approach to the modelling of common components in long memory processes is introduced. The approach is based on a two-step procedure relying on Fourier transform methods (first step) and principal components analysis (second step). Differently from other available methods, it allows the...

Vector autoregressive moving-average (VARMA) processes are suitable models for producing linear forecasts of sets of time series variables. They provide parsimonious representations of linear data generation processes. The setup for these processes in the presence of stationary and cointegrated...

The present work attempts to evaluate the advantages inherent to the use of exogenous variables highly correlated to the electric load, for the forecast of future demand. Here we utilize time series models of the auto-regressive moving average types incorporating seasonal treatment and exogenous...

We propose a general class of Markov-switching-ARFIMA processes in order to combine strands of long memory and Markov-switching literature. Although the coverage of this class of models is broad, we show that these models can be easily estimated with the DLV algorithm proposed. This algorithm...