Feature extraction, classification and forecasting of time series signal using fuzzy and garch techniques

Time series signal is a continuous signal which varies continuously with respect to time. The information content in these signals can be used for Feature Extraction, Classification and forecasting.The purpose of Feature Extraction is to reduce the dimension of feature space and achieving better performances. The Features are extracted based on the mathematical calculations like Average, Maximum, Minimum, Standard Deviation and Variance. The Classification of extracted features is carried out by Fuzzy Rule based Selection System. In this paper mamdani based Fuzzy System is used to achieve accurate results. Based on feature extracted data the Fuzzy System generates a fuzzy score and the Classifier Algorithm classifies the feature extracted signals as Good, Bad and Best signals.The purpose of Forecasting of a time series signal is to extracts some new useful information contained in large datasets. The Forecasting of time series signal is achieved by using GARCH model. GARCH model has a good prediction power and the achieved performance differs from one period of time to another, depending on the volatility structure of the time series signal.