Time Series Model

Time series models aim to analyze and predict sequential data points over time, with primary objectives focused on accurate forecasting and insightful understanding of underlying patterns. Current research emphasizes improving model explainability, incorporating external information (like text or contextual data from related series) to enhance prediction accuracy, and developing scalable architectures like transformers and neural networks (including LSTMs, RNNs, and variations) for handling increasingly complex and high-dimensional datasets. These advancements have significant implications across diverse fields, from finance and healthcare to climate modeling and engineering, enabling more informed decision-making and improved predictions in various real-world applications.