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Finding Opportunity Windows in Time Series Data Using the Sliding Window Technique: the Case of Stock Exchanges

Author

Listed:
  • Gürsakal Necmi

    (Fenerbahçe University, Faculty of Engineering and Architecture, Turkey)

  • Yilmaz Fırat Melih

    (Dokuz Eylül University, Institute of Social Sciences, Turkey)

  • Uğurlu Erginbay

    (Istanbul Aydın University, Department of International Trade, Turkey)

Abstract

Data have shapes, and human intelligence and perception have to classify the forms of data to understand and interpret them. This article uses a sliding window technique and the main aim is to answer two questions. Is there an opportunity window in time series of stock exchange index? The second question is how to find a way to use the opportunity window if there is one. The authors defined the term opportunity window as a window that is generated in the sliding window technique and can be used for forecasting. In analysis, the study determined the different frequencies and explained how to evaluate opportunity windows embedded using time series data for the S&P 500, the DJIA, and the Russell 2000 indices. As a result, for the S&P 500 the last days of the patterns 0111, 1100, 0011; for the DJIA the last days of the patterns 0101, 1001, 0011; and finally for the Russell 2000, the last days of the patterns 0100, 1001, 1100 are opportunity windows for prediction.

Suggested Citation

  • Gürsakal Necmi & Yilmaz Fırat Melih & Uğurlu Erginbay, 2020. "Finding Opportunity Windows in Time Series Data Using the Sliding Window Technique: the Case of Stock Exchanges," Econometrics. Advances in Applied Data Analysis, Sciendo, vol. 24(3), pages 1-19, September.
    • Handle: RePEc:vrs:eaiada:v:24:y:2020:i:3:p:1-19:n:1
    DOI: 10.15611/eada.2020.3.01
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    References listed on IDEAS

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    1. Spyros Makridakis & Evangelos Spiliotis & Vassilios Assimakopoulos, 2018. "Statistical and Machine Learning forecasting methods: Concerns and ways forward," PLOS ONE, Public Library of Science, vol. 13(3), pages 1-26, March.
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    More about this item

    Keywords

    time series; data science; patterns; sliding window;
    All these keywords.

    JEL classification:

    • C22 - Mathematical and Quantitative Methods - - Single Equation Models; Single Variables - - - Time-Series Models; Dynamic Quantile Regressions; Dynamic Treatment Effect Models; Diffusion Processes
    • C53 - Mathematical and Quantitative Methods - - Econometric Modeling - - - Forecasting and Prediction Models; Simulation Methods
    • C58 - Mathematical and Quantitative Methods - - Econometric Modeling - - - Financial Econometrics

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