Time Series Forecasting using Artificial Neural Networks

Time series datasets contain a collection of observations in time and this additional temporal component differentiates time series with normal datasets. Studies of forecasting time series datasets are particularly popular as they improve financial planning, sales prediction, average rainfall forecast, internet traffic rates, business trend forecasting, weather forecast, contagious disease spread, etc. Time series forecasting facilitates the decision process in various domains such as business by predicting timely sales information thereby improving management of product manufacturing output. In medical domain, predicting patient health improves medical treatment. The environmental and physical time series forecasting such as rainfall, disasters prediction would improve the contingency plans. On an overall basis time series prediction improves the way humans live. Artificial neural networks are increasingly becoming successful in predicting the time series data through regression-based models. In this article, a benchmark time series model called Mackey-Glass chaotic time series is predicted using feedforward multi-layer neural network trained using the error backpropagation algorithm. The architecture and algorithm proposed in this article can easily be extended to any real-world time series prediction requirements.