Time series analysis for financial forecasting

Abstract

With growing economies the financial world spends billions in terms of expenses. These expenditures are sometimes defined as budgets and other times as 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 trend with effective RMSE but machine learning can be utilized to do so. 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 the forecasting purposes along with a statistical models like ARIMA for comparative analysis. In this study the experiments are done over the data set of Pakistan FMCG Industry on Sales and Quantity with respect to their Date and weather effect on Sales. Results of this study demonstrate that the proposed model was able to predict the expenses with better RMSE than that of the classical statistical model like ARIMA.