Forecasting Stock Time-Series using Data Approximation and Pattern Sequence Similarity

Time series analysis is the process of building a model using statistical techniques to represent characteristics of time series data. Processing and forecasting huge time series data is a challenging task. This paper presents Approximation and Prediction of Stock Time-series data (APST), which is a two step approach to predict the direction of change of stock price indices. First, performs data approximation by using the technique called Multilevel Segment Mean (MSM). In second phase, prediction is performed for the approximated data using Euclidian distance and Nearest-Neighbour technique. The computational cost of data approximation is O(n ni) and computational cost of prediction task is O(m |NN|). Thus, the accuracy and the time required for prediction in the proposed method is comparatively efficient than the existing Label Based Forecasting (LBF) method [1].Comment: 11 page

Ant Possibilistic Fuzzy Clustered Forecasting on High Dimensional Data

ABSTRACT: Stock market plays a significant role and has greater influence on basic economic energies of a country. Rapid changes in the stock exchange market with high dimensional uncertain data make the investors to look for effective forecasting using prediction mining techniques. The high dimensional stock data are classified into profitability, stability, cash flow and growth rate but does not deal completely with uncertain attribute values. On the other hand with large amount of uncertainty, the stock attributes and classes are not included simultaneously with the conditional probabilistic (i.e., Fuzzy set) distributional functions. Moreover, the test Possibilistic approaches (i.e., predictive mining) is not carried out on genuine uncertain data. So, the research pay attention on solving the forecasting problem with predictive data mining approach and helps the investors to select suitable portfolios. To forecast complex high dimensional uncertain data, Ant Possibilistic Fuzzy Clustered Forecasting (AP-FCF) method is proposed in this paper. AP-FCF method avoids the repeating mistake on uncertain stock attributes and classes and provides domain knowledge to the investors according to the current feature salience