Leveraging AI for Accurate Time Series Forecasting

Abstract

This study seeks to develop a robust model for forecasting time series data, with an eye towards complex temporal datasets. Accurate forecasting in time series analysis is a function of past information and constitutes a basis for unsupervised machine learning. With deep learning techniques such as neural networks, this work seeks to provide high accuracy over traditional approaches in time series forecasting. Such complex techniques have a significant impact in overcoming complications in forecasting in areas such as weather trends, consumption of energy, and financial trends in the marketplace. Out of such techniques, Artificial Neural Networks have been seen to outshine alternatives such as Long Short-Term Memory networks in working with complex temporal relationships. In this work, an opportunity for leveraging complex AI techniques towards enhancing accuracy and dependability in forecasting in a time series is focused on