Defining the Settings of a Time Series Predictive Model Using a Dataset as Data Source

Before you train your Time Series predictive model using a dataset as data source, you need to specify how you want your predictive model to be trained through the Settings panel.

The following sections mirror the sections of the Settings pane you need to complete to create your predictive model.

Note

Don't forget that the date formats in your time series data source must be:

YYYY-MM-DD
YYYY/MM/DD
YYYY/MM-DD
YYYY-MM/DD
YYYYMMDD
YYYY-MM-DD hh:mm:ss

where YYYY stands for the year, MM stands for the month, DD stands for the day of the month, hh stands for hour, mm stands for minutes, and ss stands for seconds.

Example

January 25, 2018 will take one of the following supported formats:

2018-01-25
2018/01/25
2018/01-25
2018-01/25
20180125

General

Settings	Action	Additional Information
Description	Enter what your predictive model is trying to do.	For example, forecast product sales by city.
Times Series Data Source	Browse and select the data source that contains your historical data.
Edit Column Details	Check and update if necessary the columns contained in your data source.	You might need to check the statistical type if you cannot select it as your target at next step.

Predictive Goal

Settings	Action	Additional Information
Target	Select the numeric column containing the data you want to get predictive forecasts for.
Date	Select the column that contains the dates of observations for the time series.
Number Of Forecast Periods	Select the number of predictive forecasts you want.	See How Many Forecasts can be Requested?.
Entity	Select up to five columns from your data source for which you want to get distinct forecasts. This field is optional.	This corresponds to identify each entity that you want to get predictive forecasts for. The predictive model will capture specific behaviors for each entity and will generate distinct predictive forecasts.

Predictive Model Training

Settings	Action	Additional Information
Train Using	Select whether you want to train your predictive model using all observations or a window of observation. If you choose to use a window of observations you'll need to specify the window size you want to use.	It can be useful to define the range of observations that will be used to train the predictive model. You may want to ignore very old observations or inappropriate observation to avoid that your predictive model learns based on obsolete/inappropriate behavior. Example For example, if you want to forecast travel costs for next year, you might want to ignore a couple of months in your past data where travel has been frozen for budget reasons.
Until	Select whether you want to train your predictive model until the last observation or another date of your choice.	Last Observation: Let the application use the last training reference date as a basis. User-Defined Date: You select a specific date (available in the dataset).
Force Positive Forecasts	Switch the toggle on if you want to get positive forecasts only.	This turns negative predictive forecasts to zero. This can be useful when predictive forecasts only make sense as positive predictive forecasts. For example, if you need to forecast the number of sales for one of your main product by major cities for a region. It makes no sense to get negative values. Either you sell a number of products or you sell none of them. Negative values will be turned to 0.

Settings

Action

Additional Information

Train Using

Select whether you want to train your predictive model using all observations or a window of observation.

If you choose to use a window of observations you'll need to specify the window size you want to use.

It can be useful to define the range of observations that will be used to train the predictive model. You may want to ignore very old observations or inappropriate observation to avoid that your predictive model learns based on obsolete/inappropriate behavior.

Example

For example, if you want to forecast travel costs for next year, you might want to ignore a couple of months in your past data where travel has been frozen for budget reasons.

Until

Select whether you want to train your predictive model until the last observation or another date of your choice.

Last Observation: Let the application use the last training reference date as a basis.

User-Defined Date: You select a specific date (available in the dataset).

Force Positive Forecasts

Switch the toggle on if you want to get positive forecasts only.

This turns negative predictive forecasts to zero. This can be useful when predictive forecasts only make sense as positive predictive forecasts. For example, if you need to forecast the number of sales for one of your main product by major cities for a region. It makes no sense to get negative values. Either you sell a number of products or you sell none of them. Negative values will be turned to 0.

Click Train & Forecast button. Thanks to the generated reports, you can analyze the predictive model performance and decide if you need to further refine your predictive model or if you can use the predictive forecasts with confidence. For more information, see Analyzing the Results of Your Time Series Predictive Model.