This chapter reviews inference about large autoregressive or moving average roots in
univariate time series, and structural change in multivariate time series regression. The …

STAMP 6.0 is the new Windows version of the program designed for modelling and
forecasting time series using the structural time series approach. With this approach one …

This paper is to produce different scenarios in forecasts for international tourism demand, in
light of the COVID-19 pandemic. By implementing two distinct methodologies (the Long …

Forecasting is a common data science task that helps organizations with capacity planning,
goal setting, and anomaly detection. Despite its importance, there are serious challenges …

We introduce NeuralProphet, a successor to Facebook Prophet, which set an industry
standard for explainable, scalable, and user-friendly forecasting frameworks. With the …

Generating forecasts for time series with multiple seasonal cycles is an important use case
for many industries nowadays. Accounting for the multiseasonal patterns becomes …

" Introduction God made the integers, all the rest is the work of man.-Kronecker. This book is
concerned with models of event counts. An event count refers to the number of times an …

This new edition updates Durbin & Koopman's important text on the state space approach to
time series analysis. The distinguishing feature of state space time series models is that …

This book provides a formal analysis of the models, procedures, and measures of economic
forecasting with a view to improving forecasting practice. David Hendry and Michael …

The present photovoltaic (PV) power generation systems are globally facing the irregularity
problem in the distribution of PV generation. In particular, the exact PV power forecasting is …