The following introduction is to allow viewers to understand the basic concepts and practical implementation of neural nets towards a financial time series. I will not go too deep into detail about the mathematics behind the neural net at the moment. My goal is to get you to understand practical details about how to actually implement a neural net using simple tools and models. We will start with a simple model to understand a basic time series. The time series waveform is a simple sine wave with the period set to 30 days. It is implemented in excel as a source file to be processed in any Machine Learning capable software.

Weka is a collection of machine learning algorithms for data mining tasks. The algorithms can either be applied directly to a dataset or called from your own Java code. Weka contains tools for data pre-processing, classification, regression, clustering, association rules, and visualization. It is also well-suited for developing new machine learning schemes.

An overview paper discussing some issues with time series analysis in particular classification and dimensional reduction techniques for similarity & indexing.