Journal of Artificial Intelligence, Machine Learning and Soft Computing

Abstract

Time-series and sequential data are ubiquitous in fields ranging from finance and healthcare to weather forecasting and IoT sensor networks. Analyzing such data presents unique challenges due to temporal dependencies, non-stationarity, and complex patterns that evolve over time. Machine learning (ML) methods, including traditional statistical approaches, deep learning architectures, and hybrid models, have demonstrated significant promise in handling these challenges. This paper provides a comprehensive review of the current state-of-the-art ML techniques for time-series and sequential data, focusing on their applications, strengths, and limitations. It also explores recent advances in sequence modeling, including recurrent neural networks (RNNs), long short-term memory networks (LSTMs), gated recurrent units (GRUs), and transformer-based models. Finally, we highlight key challenges, future research directions, and practical considerations for deploying ML solutions in time-series forecasting and sequential data analysis.

Keywords: Time-Series Analy, Sequential Data, Machine Learning, Recurrent Neural Networks, LSTM, Transformer Models, Forecasting, Temporal Dependencies