Autoregressive (AR) Model Calculator
Unlock insights from your time series data by predicting future values using the Autoregressive (AR) Model.
Input Parameters
Enter your time series data and specify the lag order for the Autoregressive model.
Enter comma-separated numeric values representing your time series data.
Specify the order of the autoregressive model (a positive integer).
Predicted Values
AR Coefficients
Visualization
Understanding Autoregressive (AR) Models
Autoregressive (AR) models are a type of time series forecasting method that predicts future values based on past values. In simpler terms, an AR model of order 'p' assumes that the current value is linearly dependent on its 'p' previous values. This tool helps you calculate these predictions and the coefficients of the AR model. For example, in an AR(1) model, the formula is roughly: xt = c + φ1xt-1 + εt, where xt is the value at time t, φ1 is the AR coefficient, c is a constant, and εt is white noise. Use this tool to analyze trends and make forecasts in various fields like finance, weather, and signal processing.