Time-series Data Preparation organizes and formats transactional data into time-series data to predict trends and seasonality in the data.

Transactional data is timestamped data recorded over a period at no specific frequency, while time-series data is timestamped data recorded over a period at a particular frequency. The frequency or time interval can be from seconds to yearly or any other decided interval.

Transactional data is converted into time-series data by attributing frequency to the data. This is achieved by bundling or aggregating the transactional data into time-series data of the selected interval. Transactional data, when attributed with frequency, can be analyzed as time-series data.

Why is Time-series Data Preparation required

You can predict trends and seasonal variations in the time-series data that are not visible in transactional data.

How is Time-series Data Preparation done in rubiscape

Using the Rubiscape Time-series Data Preparation feature, you can analyze your time-series data by performing the following tests.

Accumulation
Missing value imputation
Transformation
Differencing

The feature converts the irregularly recorded timestamped data into data at the time interval defined by you. You can then run either one or more of the four data preparation tests in rubiscape, in any order you want. You can also run all four available tests simultaneously. When all the tests are selected to run, the tests are executed in the order – accumulation, missing value imputation, transformation, and differencing.

In accumulation, the time-series data is aggregated in the selected time interval.
In missing value imputation, the missing values in the data are removed or replaced in the accumulated time-series data.
In transformation, the time-series data is normalized. The data is rescaled from the original range to a new range between 0 and 1.

In differencing, the seasonality in the time-series data is identified and removed to make the data stationary.

Time-series Data Preparation is located under Forecasting ( ), in the task pane on the left. Use the drag-and-drop method to use the algorithm in the canvas. Click the algorithm to view and select different properties for analysis.

Refer to Properties of Time-series Data Preparation.

Time-series Data Preparation is an algorithm in which a given data is converted into time-series data of the selected interval in a specific time format. You cannot use this algorithm unless you have selected the time format.

Properties of Time-series Data Preparation

The available properties of time-series Data Preparation in Forecasting are as shown in the figure given below.

The table given below describes the different fields present in the properties of time-series Data Preparation in Forecasting.

Field		Description	Remark
Task Name		It displays the name of the selected task.	You can click the text field to edit or modify the name of the task as required.
Time ID Variable		It allows you to select the interval type variable for which we need to process the dependent or target variable's values.	Only one data field can be selected. If selected, only an interval type variable should be selected. Variables with numerical value are not available.
Target Variable		It allows you to select the experimental or predictor variable(s).	Only one data field can be selected. Variables with only numerical value are available.
Group By		It allows you to select the variable for which you want to group the data.	Multiple data field can be selected. Only categorical type variables are available.
Advanced	Interval	It allows to select the interval for the accumulation of data in the accumulation test.	Available options are – Day Week Month Quarter Year The default value is Month.
	Time Format	It allows to select the time format to display the result of the selected tests.	–
	Start Time	It allows to set the start time of the time-series data.	The default value is None. The default setting specifies that the time-series data uses its own start time.
	End Time	It allows to set the end time of the time-series data.	The default value is None. The default setting specifies that the time-series data uses its own end time.

Notes:

If you do not select the time format, the workbook gives an error when you click Save.
If you want to analyze sequential data without using the time column, you need not select the Time ID Variable data field.
You can analyze sequential data without the time column by executing the time-series data preparation tests to impute the missing values in the data, predict the next values, transform the data, and make the data stationary.
When you do not select the Time ID Variable data field, the Accumulation test checkbox is disabled for selection in the Result.

Time-series Data Preparation Tests in Forecasting

The different tests available in Time-series Data Preparation under Forecasting are given below.

Accumulation
Missing Value
Transformation
Differencing

Accumulation

Accumulation
Description	Accumulation is data aggregation in the time domain. Accumulation combines the data within the same time interval to give a summary output for that period.
Why to use	The data accumulation process is used to Convert time-series data with no fixed time interval into time-series data with a fixed time interval (weekly, monthly, or yearly). Convert time-series data with fixed time intervals into time-series data of a higher frequency time interval (for example, daily to monthly) or lower frequency time interval (for example, monthly into weekly, yearly into monthly).
When to use	To discover trends and seasonal variations based on time-series data.	When not to use	On non-interval data.
Prerequisites	The selected independent variable should be of interval type. The time interval for the data to be analyzed should be specified.
Input	Any dataset that contains a time interval.	Output	The summarized time-series data based on the selected time interval.
Statistical Methods used	Sum Mean Minimum Median Maximum	Limitations	Not applicable for sequential datasets (that is, data that does not include the datetime column).

Functions of Accumulation Test

The table given below describes the functions of the Accumulation test.

Function	Description	Remark
Sum	It gives the accumulation of the time-series data by the sum of the values in the given interval.	It can be performed only on numerical data.
Mean	It gives the accumulation of the time-series data by the mean of the values in the given interval.	It can be performed only on numerical data.
Minimum	It gives the accumulation of the time-series data by the minimum value in the given interval.	It can be performed only on numerical data.
Median	It gives the accumulation of the time-series data by the median value in the given interval.	It can be performed only on numerical data.
Maximum	It gives the accumulation of the time-series data by the maximum value in the given interval.	It can be performed only on numerical data.

Missing Value

Missing Value
Description	The time-series data may contain missing values that need to be imputed. The time-series missing value interpretation imputes the missing information in time-series data. The time-series missing value interpretation is performed only on missing values. The values present in the dataset remain unchanged.
Why to use	To impute missing values in the time-series data.
When to use	For analysis of time-series data without losing the variation in the data.	When not to use	When data do not contain any missing values.
Prerequisites	The time interval for the data to be analyzed should be specified.
Input	Time-series data with fixed time interval or time-series data.	Output	A complete time-series data for the specified time interval having no missing values.
Statistical Methods used	Mean Median Min Max Remove Constant Random	Limitations	It does not account for the uncertainty in the imputations. It can introduce bias in the data.

Functions of Missing Value Test

The table given below describes the functions of the Missing Value test.

Table: Description of Functions of Missing Value Test

Function	Description	Remark
Mean	It replaces the missing values with the mean of the non-missing values within each column separately and independently from the others.	It only works on the column level. It can only be used with numerical data.
Median	It replaces the missing values with the median of the non-missing values within each column separately and independently from the others.	It only works on the column level. It can only be used with numerical data.
Min	It replaces the missing values with the minimum value present in that column.	–
Max	It replaces the missing values with the maximum value present in that column.	–
Remove	It discards the rows that contain missing values.	It can be used for a small amount of missing data (20-30%) Removing a large amount of data may cause considerable variations in the results. If there is a large amount of missing data, it is recommended to remove the complete column (If you want to remove a column, do not select that column while analyzing).
Constant	It replaces the missing values with the constant value that you have entered.	It can only be used with numerical data. It can introduce bias in the data.
Random	It replaces the missing values with random values from that column.	It can only be used with numerical data.

Transformation

Transformation
Description	Data transformation in time-series data removes noise and improves the signal in time-series forecasting. There are different functions used to transform time-series data, useful for visualizing time-series data and modeling the time-series data. An inverse transform is applied to the predictions of a transform function applied to time-series data. This ensures that the resultant performance measures are on the same scale as the output variable. The transformation method assumes that the time-series data is positive and non-zero.
Why to use	To stabilize the variance across time in time-series data for more accurate forecasts.
When to use	To simplify patterns in time-series data by removing variation across time or by making the pattern consistent across the dataset.	When not to use	When the data is already normalized between 0 and 1.
Prerequisites	The selected independent variable should be of interval type. The time interval for the data to be analyzed should be specified.
Input	High variance time-series data.	Output	Time-series data with less variance and consistent pattern.
Statistical Methods used	Exponential Square Square Root Natural Log Log Inverse Exponential Inverse Square Inverse Square Root Inverse Natural Log Inverse Log	Limitations	Log and Natural Log functions are not applicable to data that contains zero. Since log(0) = -Inf.

Functions of Transformation Test

The table given below describes the functions of the Transformation test.

Function	Description	Remark
Exponential	It transforms the time-series by taking the exponential e (2.7183) of the values in the given interval.	It can only be used with numerical data.
Square	It transforms the time-series by taking the square of the values in the given interval.	It can only be used with numerical data.
Square Root	It transforms the time-series by taking the square root of the values in the given interval.	It can only be used with numerical data.
Natural Log	It transforms the time-series by taking the natural logarithm (logarithm to the base 10) of the values in the given interval.	It can only be used with numerical data.
Log	It transforms the time-series by taking the logarithm (logarithm to the base 2) of the values in the given interval.	It can only be used with numerical data.
Inverse Exponential	It transforms the time-series by taking the inverse exponential of the values in the given interval.	It can only be used with numerical data.
Inverse Square	It transforms the time-series by taking the inverse square of the values in the given interval.	It can only be used with numerical data.
Inverse Square Root	It transforms the time-series by taking the inverse square root of the values in the given interval.	It can only be used with numerical data.
Inverse Natural Log	It transforms the time-series by taking the inverse natural logarithm of the values in the given interval.	It can only be used with numerical data.
Inverse Log	It transforms the time-series by taking the inverse logarithm of the values in the given interval.	It can only be used with numerical data.

Differencing

Differencing
Description	Differencing is a method of transforming time-series data by removing the trends and seasonality in the data to make the time-series data stationary. In non-stationary time-series data, trends result in varying mean over time, while seasonality results in variance over time. Stationary datasets have a stable mean and variance and hence are easier to model. In differencing, the previous observation is subtracted from the current observation. In a time-series data with a lag of n, differencing converts every i^th observation of the series into its difference from the (i-n)^th observation.
Why to use	To make time-series data stationary since stationary data with stable mean and variance is easier to model.
When to use	For removing trends and seasonality in time-series data before modeling.	When not to use	When data is already stationary.
Prerequisites	The selected independent variable should be of interval type. The time interval for the data to be analyzed should be specified. A transformation test should be performed.
Input	The transformed time-series data.	Output	Stationary time-series data.
Statistical Methods used	Lag Difference	Limitations	The lag difference should be less than the number of data points in the data.

Example of Time-series Data Preparation

Consider an example of Sales time-series data. Here, the variable Date is of interval type.

In the Properties pane, the values are selected as below.

Property	Value
Time ID	Order Date
Target Variable	Sales
Group By	Segment
Interval	Month

The figure below shows the values selected.

We apply Data Preparation on the above data.

On the Result page, you can see Group as Consumer based on the selection in Group By in the Properties pane. When you select the check box for Select All Tests, all the four tests, Accumulation, Missing Value, Transformation, and Differencing are selected. Trace displays the log of the selected tests when they are executed. It logs the number of times each test starts and ends, based on the option selected in Group By.

Note:

You can also execute each test independently by selecting the corresponding check box, selecting the required values in the fields corresponding to the selected test, and then clicking Run Test.

When you click Run Selected Tests, the tests are performed based on the default function and values in the fields corresponding to each test. The result for each test is displayed in the figures given below.

Interpretation of Result of Accumulation Test

In the above figure, the Accumulation test result plot displays the monthly Sales Rate time-series data. The time-series data displayed is accumulated by the sum of the values in the interval.

Interpretation of Result of Missing Value Test

In the above figure, the Missing Value test result plot displays the monthly Sales time-series data. The time-series data displayed is imputed by replacing the missing values in the accumulated data with random values.

Interpretation of Result of Transformation Test

In the above figure, the Transformation test result plot displays the monthly Sales time-series data. The time-series data displayed is transformed using the natural logarithm (logarithm to the base 10) of the values.

Interpretation of Result of Differencing Test

In the above figure, the Differencing test result plot displays the stationary Sales time-series data. The monthly stationary time-series data is displayed by subtracting each previous observation from its current observation by the default lag value, which is 1.

Notes:

You can also change the values in the fields corresponding to each test and run all the tests at once by clicking Select All Tests.
The result of the test executed earlier is provided as an output to the next selected test.
The result of the last test executed is displayed on the Data page.
The trace displays the start time and the end time of the test(s) executed.
To view the accumulation graph for different intervals, you can change the interval from the Result page itself instead of visiting the canvas every time.

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