Time Series Regression Calculator

Time Series Regression (ARIMA/SARIMA)

Time Series Regression models temporal data by capturing patterns, trends, and seasonal effects. ARIMA (AutoRegressive Integrated Moving Average) and its seasonal extension SARIMA are powerful models for forecasting time-dependent data.

How It Works

ARIMA models have three components:

• AR (p): AutoRegressive component - uses past values to predict future values
• I (d): Integrated component - applies differencing to make the series stationary
• MA (q): Moving Average component - uses past forecast errors in the model

SARIMA extends ARIMA by adding seasonal components:

• Seasonal AR (P): Captures seasonal autoregressive effects
• Seasonal I (D): Applies seasonal differencing
• Seasonal MA (Q): Captures seasonal moving average effects
• Seasonal Period (m): The number of time steps in a seasonal cycle

When to Use Time Series Regression

Use Time Series Regression when:

• Your data is collected over time at regular intervals
• You want to forecast future values
• Your data exhibits trends, seasonality, or cyclic patterns
• You need to understand the temporal dynamics of a process
• You're working with financial, economic, weather, or sales data

How to Use This Calculator

Enter your time series data as comma-separated time,value pairs (one pair per line) or upload a CSV file. The time values should be sequential integers representing time steps. Specify the ARIMA/SARIMA parameters, and the calculator will fit the model and provide forecasts and diagnostics.