What are the differences between ARIMA and Exponential Smoothing?

What are the differences between ARIMA and Exponential Smoothing?

ARIMA (AutoRegressive Integrated Moving Average) is a model that captures trends and seasonality in time series data, while Exponential Smoothing focuses on smoothing out data points to forecast future values based on past observations.

Which method is better for short-term forecasting?

Exponential Smoothing is often preferred for short-term forecasting due to its simplicity and effectiveness in capturing recent trends.

Can ARIMA handle seasonal data?

Yes, ARIMA can handle seasonal data by using Seasonal ARIMA (SARIMA), which incorporates seasonal differencing.

What are the assumptions behind each method?

ARIMA assumes stationarity and linear relationships, while Exponential Smoothing assumes that the future is a weighted average of past observations.

How do I choose between the two methods?

Choosing between ARIMA and Exponential Smoothing depends on the data characteristics, such as seasonality, trend, and the forecasting horizon.