# Is stl a good technique for forecasting, instead of Arima?

I have a long time series(data at hourly level, for 6 years). The data is showing an hourly, a weekly, a monthly as well as a yearly trend. For this data, should I try stl(Seasonal and Trend decomposition using Loess) or arima?

I am using R for analysis and have used arima in the past. But I am not sure which technique, to use in this case, considering the data has multiple level of seasonality.

My second question is more generic. Do people, working in time series forecasting, actually use stl for forecasting purpose? I, am of the opinion, that stl is a good technique for understanding the data, and arima is a better technique for forecasting. Is this understanding correct?

• How exactly would you like to use STL for forecasting..? STL is a method for decomposing your data into the three components (i.e. exploratory data analysis and visualization) not for making any predictions. – Tim Aug 18 '17 at 7:53
• Exponential smoothing state space models can make use of STL trend data and can be used to predict time series by adding the last season of seasonal data. Maybe this is what you want? – AlexR Aug 18 '17 at 7:57
• Stl is not a forecasting technique, STL helps you decompose time series data into seasonal, trend and mouse components. STL as it is implemented in R does not have the ability to handle multiple seasonalities which means it is not suitable for handling hourly data. – forecaster Aug 18 '17 at 10:18