Exponential Smoothing Methods for Detection of the Movement of Stock Prices
Shaik Shahid1, SK. Althaf Rahaman2

1Shaik Shahid *, PG Student, Department of CS, GIS, GITAM (Deemed to be University), Visakhapatnam, India.
2SK.Althaf Rahaman, Assistant Professor, Department of CS, GIS, GITAM (Deemed to be University), Visakhapatnam, India.
Manuscript received on January 02, 2020. | Revised Manuscript received on January 15, 2020. | Manuscript published on January 30, 2020. | PP: 1220-1222 | Volume-8 Issue-5, January 2020. | Retrieval Number: E6409018520/2020©BEIESP | DOI: 10.35940/ijrte.E6409.018520

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© The Authors. Blue Eyes Intelligence Engineering and Sciences Publication (BEIESP). This is an open access article under the CC BY-NC-ND license (http://creativecommons.org/licenses/by-nc-nd/4.0/)

Abstract: Business Intelligence is a set of processes, architecture and technologies that convert raw data into meaningful information. BI has a direct impact on an organization’s strategic statistical and operational business decisions. In BI one of the most interesting areas is time series data analysis to predict are stock prices. Prediction and analysis of stock market data has got an important role in today’s economy. The aim of this paper is to predict the daily previous closing stock prices of the major tech giants of NSE (i.e. HCLTECH and TCS), using information from the historical data with the help Exponential Smoothing Methods. The historical stock prices of the stated companies for three years will be used for the training and testing of the methods. It is found that Holt-Winter’s method of exponential smoothing the given the best results out of the other exponential smoothing methods.
Keywords: Exponential Smoothing, Holt’s Exponential Smoothing, Time Series Data Analysis, Winter’s Exponential Smoothing s.
Scope of the Article: Structural Reliability Analysis.