Most forecasting models often fail to produce appropriate forecasts because they are built on the assumption that data is being generated from only one stochastic process. However, in many real world problems, the time series data are generated from one stochastic process initially and then...

Different data frequency is a common problem in many research fields; therefore, it should be handled before a particular study is well under way. Many novel ideas including disaggregation techniques, which are the major interest of this study, have been suggested to mitigate the nuisances of...

The state space model is widely used to handle time series data driven by related latent processes in many fields. In this article, we suggest a framework to examine the relationship between state space models and ARIMA models by examining the existence and positive-definiteness conditions...

The repeated occurrences of interventions in observations make most forecasting models fail to produce appropriate forecasts. The purpose of this study is to propose the adaptive forecasting procedure based on sequential identifications of interventions and adjusting forecast to them in general...