IMPLEMENTATION OF HIERACHICAL TEMPORAL MEMORY FOR STOCK PREDICTION

Nigerian Stock Exchange is the institution empowered by the Nigerian law to Manage and regulates the activities of the Nigerian capital market and therefore a key player role as they set the rules of the game despite the volatile and unpredictable nature of the market. The use of machine learning algorithms to predict the future values and other variables in the market through the use of time series data has proven to be of immense advantage to market players globally in the last two decades. In this work, the time serial movement of stock prices over a period of time extracted from daily official list of Nigeria Stock Exchange is used to predict the future values of other variables through the use of time series data and moving average methodology. Digital signal processing based on a biological neural network called Hierarchical Temporal Memory (HTM) was used for stock price data encoding and predictions and show high performance accuracy.