Modeling and Simulation of ‘Univariate and Multivariate analytics’ by applying ‘Deep Learning and Machine Learning’ Application of Support Vector Regression, Random Forest, K-Nearest Neighbors, Long Short-Term Memory and Gated Recurrent Units Algorithms f

The research focuses on the modeling and simulation of univariate and multivariate time series analysis by applying machine learning (ML) and deep learning (DL) techniques for accurate forecasting. The study includes the individuals and combined application of algorithms such as Support Vector Regression (SVR), Random Forest (RF), K-Nearest Neighbors (KNN), Long Short-Term Memory (LSTM), and Gated Recurrent Units (GRU) in the context of neural networks. These techniques are evaluated for their analytical performance in a number of time series estimation settings. ML approaches are known for their simplicity and efficiency in small data sets. On the other hand, DL approaches require larger data sets and more computational resources, but offer higher accuracy and flexibility in demanding setups. The study concludes by highlighting the importance of choosing the right algorithm based on the nature of the data and the forecast object. The results provide valuable insights for applications in finance, energy, healthcare, and other fields where time series forecasting plays an important role. Future research may consider hybrid and interpretability-enhanced approaches to develop applications of these models in real-world settings. In this study, for the forecasting specification especially Univariate analysis, the demonstrations show the actual values ​​for the past 15 days and the forecasts for each model for the next 10 days.

Univariate, Multivariate, SVR, RF, KNN, LSTM, GRU, Time Series, Neural networking

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