Alteryx makes it easy for analysts to perform time series forecasting using the R tool. In this blog, I will be running the Holt-Winters forecasting, also known as the Holt-Winters method or triple exponential smoothing using R in Alteryx. Holt-Winters is a popular time series forecasting technique. It is used to forecast future values based on historical data and is particularly effective for data with trends and seasonality.
The Holt-Winters method takes into account three components of a time series: level, trend, and seasonality. It uses exponential smoothing to estimate the level, trend and seasonality components and make predictions. Here’s the steps in R:
- Connect to the ‘Daily Vitamins Sales.xls’ data and transform the dates to monthly.
- Bring in the R tool into the canvas. In the configuration, these are the codes that I had used to read the data and convert it into a data frame. We will write out the output in anchor output #1.monthly_sales = read.Alteryx(“#1″, mode=”data.frame”)
write.Alteryx(monthly_sales, 1)
3. These are the library packages that we will be using. Type them into the the configuration box.
4. Convert the data into a time series object first. The codes are:
monthly_sales_ts <- ts(monthly_sales$total, frequency=12, start=c(2019,7))
5. To fit the Holt-Winters model, I used the following codes in R:
fit_holtwinters <- hw(monthly_sales_ts, seasonal=’additive’)
print(summary(fit_holtwinters))
plot(fit_holtwinters)
checkresiduals(fit_holtwinters)
6. Finally, to write out the model’s summary, we have to convert it into a data frame first. We will write out the summary into output anchor #2.
df_naive<-data.frame(fit_holtwinters)
write.Alteryx(df_holtwinters, 2)
7. The output for the forecasts are as follows and I am excited that we are able to do this simply in Alteryx.
8. There you have it folks. Integrating with R on Alteryx is simple and easy. I prefer the Holt-Winters method for any time series forecast as it can be used to forecast future values by projecting the level, trend, and seasonality forward. Thanks for reading my blog!
