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Forecasting Stock Time-Series using Data Approximation and Pattern Sequence Similarity | | R. H. Vishwanath
; S. Leena
; K. C. Srikantaiah
; K. Shreekrishna Kumar
; P. Deepa Shenoy
; K. R. Venugopal
; S. S. Iyengar
; L. M. Patnaik
; | | Date: |
10 Sep 2013 | | Abstract: | 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]. | | Source: | arXiv, 1309.2517 | | Services: | Forum | Review | PDF | Favorites | |
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