ARIMA vs SARIMA: Model flexibility
When it comes to model flexibility, ARIMA and SARIMA offer a trade-off:
- ARIMA: ARIMA is simpler, with fewer parameters to estimate. This makes it more adaptable to various non-seasonal data patterns. It can handle trends, cycles, and random fluctuations without being overly specific about the underlying process.
- SARIMA: SARIMA introduces additional parameters for seasonality, making it less flexible for purely non-seasonal data compared to ARIMA. It might become overly complex for straightforward forecasting tasks. The true strength of SARIMA lies in its ability to model recurring seasonal cycles. This makes it significantly more flexible for data exhibiting seasonality. It can capture the impact of past seasonal values on future forecasts, leading to more accurate predictions.
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