A Similarity-Based Approach for Financial Time Series Analysis and Forecasting
Financial time series analysis have been attracting research interest for several years. Many works have been proposed to perform economic series forecasting, however, it still is a hard endeavor to develop a general model that is able to handle the chaotic nature of the markets. Artificial intelligence methods such as artificial neural networks and support vector machines arose as promising alternatives, but they hide the processing semantics, limiting the result interpretation. In addition, one of the main drawbacks of the existing solutions is that they usually cannot be easily employed as building blocks of new analysis tools. This paper presents a new approach to financial time series forecasting based on similarity between series patterns using a database-driven architecture. We propose a new feature extractor based on visual features associated with a boosted instance-based learning classifier to predict a share’s behavior, thus improving the human analyst understanding and validation of the results. The analysis is defined through extended SQL instructions and executed over a fast and scalable engine, which makes our solution adequate to provide data analysis support for new applications handling large time series datasets. We also present experiments performed on data obtained from distinct market shares. The achieved results show that our approach outperformed existing methods in terms of accuracy, running time and scalability.
KeywordsFinancial time series Time series forecasting Similarity retrieval Classifiers Methods
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- 1.Aha, D., Kibler, D.: Instance-based learning algorithms. Machine Learning 6, 37–66 (1991) C. Willian, J. Granger Google Scholar
- 3.Barioni, M.C., Razente, H., Traina, A., Traina Jr., C.: SIREN: a similarity retrieval engine for complex data. In: VLDB, pp. 1155–1158 (2006)Google Scholar
- 5.Box, G.E.P., Jenkins, G.: Time Series Analysis, Forecasting and Control. Holden-Day, Incorporated (1990)Google Scholar
- 6.Chakrabarti, D., Faloutsos, C.: F4: large-scale automated forecasting using fractals. In: CIKM, pp. 2–9. ACM (2002)Google Scholar
- 7.Faloutsos, C., Ranganathan, M., Manolopoulos, Y.: Fast subsequence matching in time-series databases. In: SIGMOD, pp. 419–429. ACM (1994)Google Scholar
- 9.Hassan, R., Nath, B.: Stockmarket forecasting using hidden markov model. In: ISDA, pp. 192–196. IEEE Computer Society (2005)Google Scholar
- 12.Martinez, L.C., da Hora, D.N., de, J.R., Palotti, M., Meira, W., Pappa, G.L.: From an artificial neural network to a stock market day-trading system: a case study on the bm&f bovespa. In: IJCNN, pp. 3251–3258. IEEE Press (2009)Google Scholar
- 14.Simić, D., Simić, S., Svirčević, V.: Invoicing and financial forecasting of time and amount of corresponding cash inflow. Management Information Systems 6(3), 14–21 (2011)Google Scholar
- 15.Suntinger, M.: Event-based similarity search and its applications in business analytics. Master’s thesis, Vienna University of Technology, Vienna, Austria (2009)Google Scholar
- 16.Traina Jr., C., Traina, A., Faloutsos, C., Seeger, B.: Fast indexing and visualization of metric data sets using slim-trees. IEEE TKDE 14, 244–260 (2002)Google Scholar
- 19.Zezula, P., Amato, G., Dohnal, V., Batko, M.: Similarity Search - The Metric Space Approach, vol. 32. Springer (2006)Google Scholar