A comparative study of different methods of predicting time series

This thesis work presents a comparative study of different methods for predicting future values of time series data and implement them to predict the currency exchange rates. The current thesis focuses mainly on two approaches in predicting a time series. One of them is the traditional statistical approach which involves building models based on certain assumptions and then applying them to do the predictions. The models considered in this thesis are multiple regression, exponential smoothing, double exponential smoothing, Box-Jenkins method, and Winter's method. The second approach is using the concept of training neural nets and pattern recognition. This involves in designing a neural network and training it using different learning methods. The learning algorithms used in the current work involves the backpropagation method, recurrent nets learning method, adaptively trained neural nets, and fuzzy learning methods. In addition to these, some methods for forecasting a chaotic time series and fractional differencing are also mentioned in the thesis. In order to compare the performances of different techniques of forecasting the future values of a time series, experiments were conducted using the exchange rates of different currencies with respect to the US dollar. These exchange rates exhibit a lot of randomness in their behaviour and hence it was very challenging to predict their future values. Different prediction zones were selected to conduct the experiments and analysis of the results have been presented towards the end of the thesis.