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Content Provider | ACM Digital Library |
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Author | Parikh, Satyen Patel, Hiral |
Abstract | The current growing world is highly invested money in financial products to gain more and more profit. The stock market prediction is too complex and dynamic in nature. The stock market prediction is possible by identifying the pattern analysis from past historical data. There are several forecasting techniques and tools are available. This present study aimed to do the performance analysis of different forecasting techniques using four analytical tools over stock dataset. The technique applied in this paper is Exponential Smoothing, Moving Average [MA] and Neural Network [NN] using the tools like Weka, Tanagra, SPSS and Neuro Intelligence [Predict Demo]. This work focus on comparative analysis and the standards used are percentage of accuracy and error rate of every applied forecasting technique. The prediction and forecasting period is seven days. The work focus on an analysis of which tool provides effective forecasting using effective technique. A data-set is also tested for normality test which is prerequisite for applying these methodologies for better accuracy. |
Starting Page | 1 |
Ending Page | 6 |
Page Count | 6 |
File Format | |
ISBN | 9781450339629 |
DOI | 10.1145/2905055.2905186 |
Language | English |
Publisher | Association for Computing Machinery (ACM) |
Publisher Date | 2016-03-04 |
Publisher Place | New York |
Access Restriction | Subscribed |
Subject Keyword | Exponential smoothing Moving average Forecasting technique Prediction Neural network Normality Knowledge extraction |
Content Type | Text |
Resource Type | Article |
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