Clustering and prediction of time series data
For years people have been looking at the stock market and wondered if it was possible to figure out the growth of the prices. The stock market, like most other time series phenomenon, runs on cycles. After every bull market, there is a bear market and after every bear, there will be a bull market....
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| Format: | Final Year Project |
| Language: | English |
| Published: |
2014
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| Online Access: | http://hdl.handle.net/10356/61453 |
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| Institution: | Nanyang Technological University |
| Language: | English |
| Summary: | For years people have been looking at the stock market and wondered if it was possible to figure out the growth of the prices. The stock market, like most other time series phenomenon, runs on cycles. After every bull market, there is a bear market and after every bear, there will be a bull market. However despite knowing what will eventually happen, people are not able to predict when it will happen by using a standard mathematical formula and applying it to every single stock. In this paper I will make use of the method on segmentation and prediction of time series data to analyse the stock market. With these objectives in mind, I will be using the Perceptually Important Points approach to segment the stock market data and use the Time Delay Neural Network to train and thereafter predict the time series data. Past stock market data will be used to train the data and also to compare the accuracy of the programme. The results will be used to check for its validity and also suggest improvements for the time series analysis in subsequent projects |
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