Technical Analysis on Financial Time Series Data Based on Map-Reduce Programming Model: A Case Study Eşle-Indirge Programlama Modeline Dayali Olarak Finansal Zaman Serisi Verileri Üzerinde Teknik Analiz: Durum Çalişmasi


Uygun Y., Erboy M. O., AKTAŞ M. S., KALIPSIZ O., Aykurt I.

2018 International Congress on Big Data, Deep Learning and Fighting Cyber Terrorism, IBIGDELFT 2018, Ankara, Turkey, 3 - 04 December 2018, pp.92-97 identifier

  • Publication Type: Conference Paper / Full Text
  • Doi Number: 10.1109/ibigdelft.2018.8625357
  • City: Ankara
  • Country: Turkey
  • Page Numbers: pp.92-97
  • Keywords: Financial Time Series Data, Map-Reduce Programming Model, Technical Analysis, Technical Analysis Indicators
  • Yıldız Technical University Affiliated: Yes

Abstract

Technical analysis is a widely used method for forecasting the price direction on the financial time series data. This method requires the use of different number and types of analysis algorithms (technical indicators) together. Although these algorithms show successful performance on small-scalefinancial time series data, significant performance decreases are detected when the size of data increased. On the large-scale financial time series data, it is necessary to implement these algorithms based on the Map-Reduce programming model and examine the performance of the algorithms which are implemented based on this model comparatively. For this purpose, seven different indicators are studied within the scope of this study, new versions of these indicators are implemented using Map-Reduce parallel data processing model and performance comparisons are made with these algorithms. As a result of these comparisons on single-node and multi-node, significant performance gains have been obtained using Map-Reduce programming model.