Deformable Markov model templates for time-series pattern matching
Proceedings of the sixth ACM SIGKDD international conference on Knowledge discovery and data mining
Speech and Language Processing: An Introduction to Natural Language Processing, Computational Linguistics, and Speech Recognition
Introduction to Modern Information Retrieval
Introduction to Modern Information Retrieval
IEEE Transactions on Knowledge and Data Engineering
An Online Algorithm for Segmenting Time Series
ICDM '01 Proceedings of the 2001 IEEE International Conference on Data Mining
News Sensitive Stock Trend Prediction
PAKDD '02 Proceedings of the 6th Pacific-Asia Conference on Advances in Knowledge Discovery and Data Mining
Online event-driven subsequence matching over financial data streams
SIGMOD '04 Proceedings of the 2004 ACM SIGMOD international conference on Management of data
Modeling of multivariate time series using hidden markov models
Modeling of multivariate time series using hidden markov models
Introduction to Data Mining, (First Edition)
Introduction to Data Mining, (First Edition)
Improving stock market prediction by integrating both market news and stock prices
DEXA'11 Proceedings of the 22nd international conference on Database and expert systems applications - Volume Part II
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In many real world applications, decisions are usually made by collecting and judging information from multiple different data sources. Let us take the stock market as an example. We never make our decision based on just one single piece of advice, but always rely on a collection of information, such as the stock price movements, exchange volumes, market index, as well as the information from the news articles, expert comments and special announcements (e.g., the increase of stamp duty). Yet, modeling the stock market is difficult because: (1) The process related to market states (up and down) is a stochastic process, which is hard to capture by using the deterministic approach; and (2) The market state is invisible but will be influenced by the visible market information, such as stock prices and news articles. In this paper, we try to model the stock market process by using a Non-homogeneous Hidden Markov Model (NHMM) which takes multiple sources of information into account when making a future prediction. Our model contains three major elements: (1) External event, which denotes the events happening within the stock market (e.g., the drop of US interest rate); (2) Observed market state, which denotes the current market status (e.g. the rise in the stock price); and (3) Hidden market state, which conceptually exists but is invisible to the market participants. Specifically, we model the external events by using the information contained in the news articles, and model the observed market state by using the historical stock prices. Base on these two pieces of observable information and the previous hidden market state, we aim to identify the current hidden market state, so as to predict the immediate market movement. Extensive experiments were conducted to evaluate our work. The encouraging results indicate that our proposed approach is practically sound and effective.