Time series models are statistical models that examine and predict data points collected over time. These models are very helpful for comprehending and forecasting trends, patterns, and behaviors in sequential data. The basic premise in time series analysis is that the observations are time dependent, which means that the order of the data points is important. Time series models aid in the capture and interpretation of data’s temporal patterns, providing for insights into previous trends and future projections.
Time series models are typically categorized into two types: univariate models and multivariate models. Univariate time series models examine a single variable over time, whereas multivariate models examine the interdependencies of numerous variables. Autoregressive Integrated Moving Average (ARIMA) models, which capture autoregressive and moving average components, and Exponential Smoothing State Space Models (ETS), which handle trend and seasonality, are examples of common univariate models. Multivariate models, such as Vector Autoregression (VAR) and Structural Time Series Models, broaden the study to include several interacting variables, allowing for a more complete understanding of complex systems. The model chosen is determined by the nature of the data and the patterns seen, and the success of these models is dependent on their proper selection and fine-tuning.