Arize AI
Timeseries Forecasting
(Timeseries Prediction)


This example runs through how to setup a time series forecasting model in the Arize platform. In the timeseries forecasting use cases a model is run on specific run-date predicting data for a forecasting date in the future. This example will be predicting supply of a product from today, for each day, 10 days in the future.
This setup can be modified for predicting:
  • End of quarter sales run every day until end of the quarter
  • Predicting a single day in the future 100 days out - run every day
  • Predicting sales of a product every day looking out over the next 30 days
Example of Timeseries Forecasting
The example above shows a supply model that makes 10 predictions for each day looking forward from the model run-date. The lag captures how many days ahead of the model run-date the specific prediction is for, where lag 0 is the actual day of the run.

Common Observability Data for Timeseries Model

The common data that is tracked for timeseries models includes:
  • Run-date: This is the date the model is run, this can be used to filter the data.
  • Forecasting Date: This is the date and/or time we are forecasting for in the future. This by day forecast data is normally the main values we visualize in a time series graph.
  • Lag: The lag is the days ahead from the run-date. A 0 lag is the same day as the run date, a 30 day lag is 30 days ahead in the future.
Setting up TimeSeries Data
The above picture shows how the data is mapped into the Arize platform. The timestamp is the forecast date, the run-date & lag is sent in as a tag and the actual prediction is sent in as the prediction label.

Common Performance Metrics

The common metrics for timeseries forecasting are:
  • MAE
  • RMSE
  • MSE
  • MAPE
Filter options: Run-date, Lag
In the Arize Dashboard shown below, it's clear that we see an over prediction event first, then an under prediction event later. We can clearly see the magnitude of these errors based on our custom configuration.
Timeseries MAPE
The above MAE shows predictions vs actuals for various forecast dates. In many scenarios teams want filtered by Lag < 10 looking at MAE for predictions only 10 days out.


The Colab example below shows how a timeseries model is setup in the Arize platform.
Google Colaboratory