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A Robust Time Series Prediction Model Using POMDP and Data Analysis

Suho Cho 1, Seungho Cho 2, and Kin Choong Yow 3
1. Gwangju Institute of Science and Technology Department Electrical Engineering and Computer Science, Gwangju, Republic of Korea
2. Department Economics, Chungang University, Seoul, Republic of Korea
3. Gwangju Institute of Science and Technology Division of Liberal arts and Sciences, Gwangju, Republic of Korea

Abstract—One of the most important applications of information technology is to summarize data and predict new data based on existing values. For example, in stock market analysis, many investors use technical analysis tools to create a model that helps them in decision making. To minimize the uncertainties of the stock market, investors implement prediction models modified with their opinions. An ETF, which has a strong mutual connectivity between different portfolios, gets attention of the public by its low risk, intraday tradability and tax efficiency. In this paper, we propose a model in which investors’ opinion can be applied via Partially Observable Markov Decision Processes (POMDP), so that investors can intervene in the model to improve the prediction and make greater profit. Since an ETF has a strong mutual connectivity, we also use historical data to find out the relative changes between the chosen portfolios. This helps the model work better in POMDP structure. 

Index Terms—POMDP, data mining, multi-item prediction, time series technical analysis, stock market, ETFs

Cite: Sho Ooi, Tsuyoshi Ikegaya, Mutsuo Sano, Hajime Tabuchi, Fumie Saito, and Satoshi Umeda, "Attention Behavior Evaluation during Daily Living Based on Egocentric Vision," Vol. 8, No. 2, pp. 154-158, May, 2017. doi: 10.12720/jait.8.2.154-158