Industrial Financial Forecasting using Long Short-Term Memory Recurrent Neural Networks

This research deals with the industrial financial forecasting in order to calculate the yearly expenditure of the organization. Forecasting helps in estimation of the future trends and provides a valuable information to make the industrial decisions. With growing economies, the financial world spends billions in terms of expenses. These expenditures are also defined as budgets or operational resources for a functional workplace. These expenses carry a fluctuating property as opposed to a linear or constant growth and this information if extracted can reshape the future in terms of effective spending of finances and will give an insight for the future budgeting reforms. It is a challenge to grasp over the changing trends with an effective accuracy and for this purpose machine learning approaches can be utilized. In this study Long Short-Term Memory (LSTM), which is a variant of Recurrent Neural Network (RNN) from the family of Artificial Neural Networks (ANN), is used for forecasting purposes along with a statistical tool IBM SPSS for comparative analysis. In this study, the experiments are performed on the data set of Pakistan GDP by type of expenditure at current prices - national currency (1970-2016) produced by Economic Statistics Branch of the United Nations Statistics Division (UNSD). Results of this study demonstrate that the proposed model predicted the expenses with better accuracy than that of the classical statistical tools.