Probabilistic Detection of Volatility Changes within Temporal Data Sequences
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Time series modeling is a dynamic research area which has been attracted to the researcher's community over last few decades. The main aim of time series modeling is to carefully collect and rigorously study the past observations of a time series to develop an appropriate model which describes the intrinsic structure of the series. These models are used to generate future values for the series, i.e. to make forecasts. Time series forecasting thus can be termed as the act of predicting the future by understanding the past. Due to the indispensable importance of time series forecasting in numero...
Time series modeling is a dynamic research area which has been attracted to the researcher's community over last few decades. The main aim of time series modeling is to carefully collect and rigorously study the past observations of a time series to develop an appropriate model which describes the intrinsic structure of the series. These models are used to generate future values for the series, i.e. to make forecasts. Time series forecasting thus can be termed as the act of predicting the future by understanding the past. Due to the indispensable importance of time series forecasting in numerous practical fields such as business, economics, finance, science and engineering, etc. and proper care should be taken to fit an adequate model to the underlying time series. It is clear that a successful time series forecasting depends on an appropriate selection of model fitting. Over the many years, a lot of efforts have been done by researchers for the development of efficient models to improve the forecasting accuracy. As a result, in the literature various important time series forecasting models have been evolved.
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