TY - CHAP
T1 - Building a Rough Sets-Based Prediction Model of Tick-Wise Stock Price Fluctuations
AU - Matsumoto, Yoshiyuki
AU - Watada, Junzo
PY - 2013
Y1 - 2013
N2 - Rough sets enable us to mine knowledge in the form of IF-THEN decision rules from a data repository, a database, a web base, and others. Decision rules are used to reason, estimate, evaluate, and forecast. The objective of this paper is to build the rough sets-based model for analysis of time series data with tick-wise price fluctuations where knowledge granules are mined from the data set of tickwise price fluctuations. We show how a method based on rough sets helps acquire the knowledge from time-series data. The method enables us to obtain IF-THEN type rules for forecasting stock prices.
AB - Rough sets enable us to mine knowledge in the form of IF-THEN decision rules from a data repository, a database, a web base, and others. Decision rules are used to reason, estimate, evaluate, and forecast. The objective of this paper is to build the rough sets-based model for analysis of time series data with tick-wise price fluctuations where knowledge granules are mined from the data set of tickwise price fluctuations. We show how a method based on rough sets helps acquire the knowledge from time-series data. The method enables us to obtain IF-THEN type rules for forecasting stock prices.
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U2 - 10.1007/1007/978-3-642-33439-9_14
DO - 10.1007/1007/978-3-642-33439-9_14
M3 - Chapter
AN - SCOPUS:84885441322
SN - 9783642334382
VL - 47
T3 - Intelligent Systems Reference Library
SP - 301
EP - 329
BT - Intelligent Systems Reference Library
ER -