When to use : ARIMA VS SARIMA
The choice between ARIMA and SARIMA boils down to whether your time series data has seasonality:
- Use ARIMA if:
- Your data has no seasonality or very weak seasonal patterns.
- Model interpretability is a priority. ARIMA’s simplicity makes it easier to understand the factors influencing forecasts.
- You’re dealing with limited data. ARIMA’s fewer parameters can be advantageous in such cases.
- Use SARIMA if:
- Your data exhibits strong seasonality, like monthly sales figures with holiday spikes or quarterly customer churn.
- You have a large dataset that captures multiple seasonal cycles. SARIMA’s ability to handle seasonality becomes more pronounced with more data.
- Forecast accuracy is your main concern. SARIMA generally leads to more accurate predictions for seasonal data.
ARIMA vs SARIMA Model
Time series data, consisting of observations measured at regular intervals, is prevalent across various domains. Accurately forecasting future values from this data is crucial for informed decision-making. Two powerful statistical models, ARIMA and SARIMA, are widely used in time series forecasting. In this tutorial, we will explore the difference between ARIMA and SARIMA models for time series forecasting, understanding their strengths, limitations, and practical applications.
Table of Content
- What is ARIMA (Autoregressive Integrated Moving Average)?
- What is SARIMA(Seasonal Autoregressive Integrated Moving Average)?
- ARIMA vs SARIMA: Seasonality
- ARIMA vs SARIMA: Model flexibility
- ARIMA vs SARIMA: Forecast accuracy
- ARIMA vs SARIMA : Use-Cases
- Difference Between ARIMA and SARIMA
- Advantages and Disadvantages of ARIMA Model
- Advantages and Disadvantages of SARIMA Model
- When to use : ARIMA VS SARIMA
- Conclusion
- ARIMA V/S SARIMA Model – FAQs