Deep Learning Using Long Short Term Memory And Gated Recurrent Unit For Forecasting Volatile And Complex Time Series Data

Abstract

Machine learning plays a vital role in the modern world. It is widely used in scientific newlineresearch, in various businesses and in the technological field. The techniques using Artificial newlineNeural Networks (ANN), which is a subset of the repertoire of machine learning techniques, newlinefind important applications in this digital era for acquiring insights from data, to automate newlineprocesses, and to enhance decision making processes for improved efficiency and accuracy. newlineDeep learning is a branch of machine learning that employs ANN s with multiple layers (deep newlinenetworks) to capture complex patterns in data. The ability to reuse information is an newlineintriguing feature of deep learning, as shown in recurrent neural networks (RNNs), a type of newlineANNs designed with additional qualities to effectively process sequential input. It has been newlinefound that RNN s are good in data prediction when dealing with complex and volatile time newlineseries data. newlineDeep neural networks excel at modeling complex, non-linear, and long-term dependencies, newlinemaking them highly effective for large-scale analytics and multi-variable scenarios. Long newlineShort-Term Memory (LSTM) and Gated Recurrent Unit (GRU) are specialized types of RNN newlinelayers designed for processing sequential data. They overcome the vanishing gradient newlineproblem in traditional RNNs by using gating mechanisms. In this research work an attempt is newlinemade to develop and implement a time series prediction application using LSTM and GRU newlinenetworks. This work aims to investigate deep learning techniques and conceptual modeling newlinefor predicting future trends based on historical patterns. newlineThe time series data considered in this work are from two different applications, namely, newline1. the stock market price prediction (a financial application) and, newline2. the energy demand prediction (an engineering application).xix newlineIn both cases, time series data are taken for a particular period (say many years or months) to newlinepredict future values. newlineThe software libraries TensorFlow, Keras, NumPy, Pandas, Scikit-learn, etc.,