Time-series data prediction using fuzzy data dredging

As information technology (I.T.) is progressing rapidly day by day a massive amount of data is emerging at a fast rate in different sectors. Data dredging provides techniques to have relevant data from a large amount of data for the task. This paper introduces an algorithm for fuzzy data dredging through which fuzzy association rules can be generated for time series data. Time series data can be stock market data, climatic observed data or any sequence data which has some trend or pattern in it. In the past many approaches based on mathematical models were suggested for dredging association rules but they were quite complex for the users. This paper emphasis on the reduction of large number of irrelevant association rules obtained providing a better platform of future prediction using fuzzy membership functions and fuzzy rules for time series data. Secondly, this paper also measures the data dispersion in time series data mainly in stock market data and shows the deviation of the stock prices from the mean of several stock price data points taken over a period of time which help the investors to decide whether to buy or sell their products. Risk investment can be predicted understanding the obtained curve in the experiment. Experiments are also carried out to show the results of the proposed algorithm.