Advance Forecasting of Multivariate Time Series using LSTM, VAR and Block Sampling Technique

Time Series Forecasting plays a critical role in wide range of physical applications, from finance to weather forecasting, climate science, trend analysis and digital communication systems etc. Accurate Forecasting and classification of Data is very crucial for decision making, system development and strategy planning. However, forecasting in these domains is relatively complicated due to the presence of imbalanced datasets like when rare and extreme events must be predicted with precision. These challenges are compounded by interconnection between multiple variables where dependencies across multiple time series can cause complexity that cannot be predicted by simple calculations. Therefore need for an enhanced approach is necessary particularly ones that can handle such intricacies of complex dependencies and imbalance in dataset. This study proposes deep learning algorithms such as LSTM (Long Short Term Memory) networks, Vector AutoRegressive (VAR) Model and block resampling in joint predictor forecast space. The objective is to evaluate these techniques with datasets, analysis and compare their results to significantly advance accuracy for time series forecasting for dynamic and interconnected systems.