Missing Time Imputation is located under
Forecasting > Data Preparation > Missing Time Imputation
Use the drag-and-drop method to use the algorithm in the canvas. Click the algorithm to view and select different properties for analysis.
Imputing missing values is an important step when dealing with data. It is also one of the steps involved in Data Analysis. In time-series analysis, missing dates play a significant role in the overall analysis. If the missing dates are untouched, the performance of many time-series machine-learning models will be affected.
The available properties of Missing Time Imputation are shown in the figure given below.
Field | Description | Remark |
It helps to execute the node. | – | |
It helps to explore the successful node. | – | |
It displays the following options in the list view.
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Task Name | It is the name of the task selected on the workbook canvas. | You can click the text field to edit or modify the task name as required. |
Time Variables | It allows you to select time variables to perform missing time imputation. |
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Frequency | Frequency options define the frequency at which time series data is measured. | Here are some commonly used frequency options:
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Select Imputation Method | It allows you to select the imputation method from the drop-list to apply for the selected data fields. | The available imputation methods are,
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Exclude Public Holiday | It excludes the public holidays while imputing the missing values. |