Open AccessExponential Smoothing Methods for Detection of the Movement of Stock Prices
Author(s) -
S. A. Md Shahid,
SK.Althaf Rahaman
Publication year - 2020
Publication title -
international journal of recent technology and engineering
Language(s) - English
Resource type - Journals
ISSN - 2277-3878
DOI - 10.35940/ijrte.e6409.018520
Subject(s) - exponential smoothing , smoothing , econometrics , stock (firearms) , exponential function , computer science , stock market , moving average , financial economics , economics , mathematics , engineering , geography , mechanical engineering , mathematical analysis , context (archaeology) , archaeology , computer vision
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.