(IJACSA) International Journal of Advanced Computer Science and Applications, Vol. 10, No. 4, 2019 88 | Page www.ijacsa.thesai.org Industrial Financial Forecasting using Long Short-Term Memory Recurrent Neural Networks Muhammad Mohsin Ali 1 , Muhammad Imran Babar 2 , Muhammad Hamza 3 , Muhammad Jehanzeb 4 , Saad Habib 5 , Muhammad Sajid Khan 6 APCOMS, Rawalpindi, Pakistan.UET, Taxila, Pakistan Abstract—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. Keywords—Financial forecasting; prediction; long-short term memory; recurrent neural networks; artificial neural networks; IBM SPSS I. INTRODUCTION Forecasting is concerned with the process of estimating the future trends by doing the analysis on the basis of historical data. Financial forecasting determines the trends by utilizing the previous data and provides valuable information to make future decisions and define strategies for financial management. Financial forecasting is highly vital as different companies and firms are closed due to bankruptcy and the obvious reason behind it was their strategies that were not well- defined and they were unable to compete their rivals. They were not able to see what is coming in the future and when it comes, they were not prepared with the consequences. This looks like a troublesome issue to comprehend however, there is a solution to every problem. As the time goes on, organizations spare their vital data in a very much characterized and appropriate way since it may be useful for them in later phases of the business to analyze the trends. This is where the solution lies. Whatever, that is occurring in the organization is being spared either it is related to their products or manufacturing or their finances. Forecasting the financial conditions, in terms of expenses, will be vital for a company to survive and that is the idea of this research. Much of the work is done in the domain of stock market [1-5], power and load [6-9], building energy consumption [10-12], electric price [13-16], weather forecasting [17, 18] and so on. However, the focus of this research is on expense forecasting of the companies to save them from bankruptcy. In this research a technique is proposed to predict the financial expenses of an organization. The data of the company are analyzed and helpful data are isolated. The legitimacy of the data is highly vital to ensure the results of the proposed intelligent technique for financial forecasting. Different machine learning (ML) techniques are used for financial forecasting. The understanding of the given outcomes is highly vital with the goal that they can be utilized later for various purposes. There are couple of imperative things to consider like the information being utilized is from dependable sources and not fabricated. Secondly, system being designed needs not to be error prone. Forecasting is never 100 % yet it should be as close as it is conceivable. Salaries, business charges, office lease, telephone charges or any other, it appears as though there's no conclusion to the costs related with maintaining a business or to a specific individual. In any case, your capacity to get a firm handle on these money outpourings can assume an imperative part in your definitive achievement or disappointment. Regardless of whether you're contemplating a startup venture or you've been doing business for some time, precisely determining your costs can profit your venture in a number of potential ways. To improve the procedure of financial forecasting there are different strategies which can be utilized to interface each sort of cost to different factors or cost drivers, for example, income or headcount, which have just been conjecture in the budgetary projections. Obviously, there will be expenses which are settled down and cannot be connected to different factors and should be assessed in total financial terms. There is a need of system that should keep all implying factors in to account and forecast expenses with logical and dependable results. Forecasting helps to create simulation in understanding problems of many sorts for example the weather, a simple analysis can lead us to predict the chances of rain in a particular way that the farmer can sow the seeds accordingly and predicting has led us to many possibilities same can be said in financial sectors and we can consider prediction of the bankruptcies on a corporate level and have counter measure ready at the time of need.