Univariate forecasting

Information

• Univariate forecasting is a time series forecasting technique that involves analyzing a single variable's past values to predict its future values.
• It assumes that the variable of interest's past values are the most relevant predictor of its future values.
• Univariate models do not consider the relationship between the variable of interest and other related variables.
• The most common approach to univariate forecasting is to fit an autoregressive integrated moving average (ARIMA) model to the variable's historical data.
• Other common univariate forecasting techniques include exponential smoothing, state-space models, and neural networks.
• Univariate forecasting is often used in situations where there is a single variable of interest and no other related variables to consider.