Investigation of Event-Prediction in Time-Series Data : How to organize and process time-series data for event prediction?

The thesis determines the type of deep learning algorithms to compare for a particular dataset that contains time-series data. The research method includes study of multiple literatures and conduction of 12 tests. It deals with the organization and processing of the data so as to prepare the data for prediction of an event in the time-series. It also includes the explanation of the algorithms selected. Similarly, it provides a detailed description of the steps taken for classification and prediction of the event. It includes the conduction of multiple tests for varied timeframe in order to compare which algorithm provides better results in different timeframes. The comparison between the selected two deep learning algorithms identified that for shorter timeframes Convolutional Neural Networks performs better and for longer timeframes Recurrent Neural Networks has higher accuracy in the provided dataset. Furthermore, it discusses possible improvements that can be made to the experiments and the research as a whole