Forecasting Symbolic Candle Chart-Valued Time Series
| Autor: | Fumitake Sakaori, Heewon Park |
|---|---|
| Rok vydání: | 2014 |
| Předmět: |
Statistics and Probability
Series (mathematics) Candlestick chart Applied Mathematics Aggregate (data warehouse) Interval (mathematics) computer.software_genre Stock market index Symbolic data analysis law.invention Data set law Modeling and Simulation Data mining Statistics Probability and Uncertainty Candle computer Finance Mathematics |
| Zdroj: | Communications for Statistical Applications and Methods. 21:471-486 |
| ISSN: | 2287-7843 |
| DOI: | 10.5351/csam.2014.21.6.471 |
| Popis: | This study introduces a new type of symbolic data, a candle chart-valued time series. We aggregate four stock indices (i.e., open, close, highest and lowest) as a one data point to summarize a huge amount of data. In other words, we consider a candle chart, which is constructed by open, close, highest and lowest stock indices, as a type of symbolic data for a long period. The proposed candle chart-valued time series effectively summarize and visualize a huge data set of stock indices to easily understand a change in stock indices. We also propose novel approaches for the candle chart-valued time series modeling based on a combination of two midpoints and two half ranges between the highest and the lowest indices, and between the open and the close indices. Furthermore, we propose three types of sum of square for estimation of the candle chart valued-time series model. The proposed methods take into account of information from not only ordinary data, but also from interval of object, and thus can effectively perform for time series modeling (e.g., forecasting future stock index). To evaluate the proposed methods, we describe real data analysis consisting of the stock market indices of five major Asian countries’. We can see thorough the results that the proposed approaches outperform for forecasting future stock indices compared with classical data analysis. |
| Databáze: | OpenAIRE |
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