What is a univariate time series, and how is it different from multivariate?

A univariate time series is a sequence of data points measured over time, focusing on a single variable. For example, tracking daily temperature readings or hourly stock prices involves collecting one value per time interval. The analysis of univariate time series typically involves understanding patterns like trends, seasonality, or noise within that single variable to make forecasts. Models like ARIMA (Autoregressive Integrated Moving Average) or exponential smoothing are commonly used here because they rely solely on the historical values of the target variable to predict future values. A classic use case is predicting monthly electricity consumption based on past usage data, where only the consumption values are used as input.

In contrast, a multivariate time series involves two or more variables measured at the same time intervals, where relationships between variables can influence predictions. For instance, predicting energy demand might require not just historical demand data but also variables like temperature, time of day, or economic indicators. Multivariate models, such as VAR (Vector Autoregression) or machine learning approaches like neural networks, account for interdependencies between variables. For example, a retailer forecasting sales might use advertising spend, competitor pricing, and holiday dates alongside historical sales data to improve accuracy. The added complexity comes from modeling how changes in one variable affect others over time.

The key difference lies in data structure and modeling goals. Univariate analysis is simpler, requiring only a single data stream, and is useful when external factors are either irrelevant or too costly to measure. Multivariate analysis, however, captures interactions between variables, which can lead to more accurate predictions when multiple factors drive outcomes. For developers, this impacts data preprocessing (e.g., aligning multiple time series), model selection (e.g., handling high-dimensional data), and computational resources. Libraries like statsmodels support univariate methods, while tools like TensorFlow or PyTorch are often used for multivariate models due to their ability to manage complex dependencies. Choosing between the two depends on the problem’s context and the availability of relevant variables.