Predicting the Future with Artificial Neural Network

Accurate prediction of future values of time series data is crucial for strategic decision making such as inventory management, budget planning, customer relationship management, marketing promotion, and efficient allocation of resources. However, time series prediction can be very challenging especially when there are elements of uncertainty including natural disaster, change in government policies and weather condition. In this research, four different multilayer perceptron (MLP) artificial neural networks have been discussed and compared with Autoregressive Integrated Moving Average (ARIMA) for this task. The models are evaluated using two statistical performance evaluation measures, Root Mean Squared Error (RMSE) and coefficient of determination (R2). The experimental result shows that a 4-layer MLP architecture using the tanh activation function in each of the hidden layer and a linear function in the output layer has the lowest prediction error and the highest coefficient of determination among the configured multilayer perceptron neural networks. In addition, comparative analysis of performance result reveals that the multilayer perceptron neural network MLP has a lower prediction error than the ARIMA model.