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EXAMPLE - Rolling Functions 2

This example describes how to use rolling statistical functions.

Functions:

Item

Description

ROLLINGAVERAGE Function

Computes the rolling average of values forward or backward of the current row within the specified column.

ROLLINGMAX Function

Computes the rolling maximum of values forward or backward of the current row within the specified column. Inputs can be Integer, Decimal, or Datetime.

ROLLINGSTDEV Function

Computes the rolling standard deviation of values forward or backward of the current row within the specified column.

ROLLINGVAR Function

Computes the rolling variance of values forward or backward of the current row within the specified column.

ROLLINGSTDEVSAMP Function

Computes the rolling standard deviation of values forward or backward of the current row within the specified column using the sample statistical method.

ROLLINGVARSAMP Function

Computes the rolling variance of values forward or backward of the current row within the specified column using the sample statistical method.

Also:

Item

Description

MERGE Function

Merges two or more columns of String type to generate output of String type. Optionally, you can insert a delimiter between the merged values.

ROUND Function

Rounds input value to the nearest integer. Input can be an Integer, a Decimal, a column reference, or an expression. Optional second argument can be used to specify the number of digits to which to round.

Source:

In this example, the following data comes from times recorded at regular intervals during a three-lap race around a track. The source data is in cumulative time in seconds (time_sc). You can use ROLLING and other windowing functions to break down the data into more meaningful metrics.

lap

quarter

time_sc

1

0

0.000

1

1

19.554

1

2

39.785

1

3

60.021

2

0

80.950

2

1

101.785

2

2

121.005

2

3

141.185

3

0

162.008

3

1

181.887

3

2

200.945

3

3

220.856

Transformation:

Primary key: Since the quarter information repeats every lap, there is no unique identifier for each row. The following steps create this identifier:

Transformation Name

Change column data type

Parameter: Columns

lap,quarter

Parameter: New type

String

Transformation Name

New formula

Parameter: Formula type

Single row formula

Parameter: Formula

MERGE(['l',lap,'q',quarter])

Parameter: New column name

'splitId'

Get split times: Use the following transform to break down the splits for each quarter of the race:

Transformation Name

New formula

Parameter: Formula type

Multiple row formula

Parameter: Formula

ROUND(time_sc - PREV(time_sc, 1), 3)

Parameter: Order rows by

splitId

Parameter: New column name

'split_time_sc'

Compute rolling computations: You can use the following types of computations to provide rolling metrics on the current and three previous splits:

Transformation Name

New formula

Parameter: Formula type

Multiple row formula

Parameter: Formula

ROLLINGAVERAGE(split_time_sc, 3)

Parameter: Order rows by

splitId

Parameter: New column name

'ravg'

Transformation Name

New formula

Parameter: Formula type

Multiple row formula

Parameter: Formula

ROLLINGMAX(split_time_sc, 3)

Parameter: Order rows by

splitId

Parameter: New column name

'rmax'

Transformation Name

New formula

Parameter: Formula type

Multiple row formula

Parameter: Formula

ROLLINGMIN(split_time_sc, 3)

Parameter: Order rows by

splitId

Parameter: New column name

'rmin'

Transformation Name

New formula

Parameter: Formula type

Multiple row formula

Parameter: Formula

ROUND(ROLLINGSTDEV(split_time_sc, 3), 3)

Parameter: Order rows by

splitId

Parameter: New column name

'rstdev'

Transformation Name

New formula

Parameter: Formula type

Multiple row formula

Parameter: Formula

ROUND(ROLLINGVAR(split_time_sc, 3), 3)

Parameter: Order rows by

splitId

Parameter: New column name

'rvar'

Compute rolling computations using sample method: These metrics compute the rolling STDEV and VAR on the current and three previous splits using the sample method:

Transformation Name

New formula

Parameter: Formula type

Multiple row formula

Parameter: Formula

ROUND(ROLLINGSTDEVSAMP(split_time_sc, 3), 3)

Parameter: Order rows by

splitId

Parameter: New column name

'rstdev_samp'

Transformation Name

New formula

Parameter: Formula type

Multiple row formula

Parameter: Formula

ROUND(ROLLINGVARSAMP(split_time_sc, 3), 3)

Parameter: Order rows by

splitId

Parameter: New column name

'rvar_samp'

Results:

When the above transforms have been completed, the results look like the following:

lap

quarter

splitId

time_sc

split_time_sc

rvar_samp

rstdev_samp

rvar

rstdev

rmin

rmax

ravg

1

0

l1q0

0

1

1

l1q1

20.096

20.096

0

0

20.096

20.096

20.096

1

2

l1q2

40.53

20.434

0.229

0.479

0.029

0.169

20.096

20.434

20.265

1

3

l1q3

61.031

20.501

0.154

0.392

0.031

0.177

20.096

20.501

20.344

2

0

l2q0

81.087

20.056

0.315

0.561

0.039

0.198

20.056

20.501

20.272

2

1

l2q1

101.383

20.296

0.142

0.376

0.029

0.17

20.056

20.501

20.322

2

2

l2q2

122.092

20.709

0.617

0.786

0.059

0.242

20.056

20.709

20.39

2

3

l2q3

141.886

19.794

0.621

0.788

0.113

0.337

19.794

20.709

20.214

3

0

l3q0

162.581

20.695

0.579

0.761

0.139

0.373

19.794

20.709

20.373

3

1

l3q1

183.018

20.437

0.443

0.666

0.138

0.371

19.794

20.709

20.409

3

2

l3q2

203.493

20.475

0.537

0.733

0.113

0.336

19.794

20.695

20.35

3

3

l3q3

222.893

19.4

0.520

0.721

0.252

0.502

19.4

20.695

20.252

You can reduce the number of steps by applying awindowtransform such as the following:

Transformation Name

Window

Parameter: Formula1

lap

Parameter: Formula2

rollingaverage(split_time_sc, 0, 3)

Parameter: Formula3

rollingmax(split_time_sc, 0, 3)

Parameter: Formula4

rollingmin(split_time_sc, 0, 3)

Parameter: Formula5

round(rollingstdev(split_time_sc, 0, 3), 3)

Parameter: Formula6

round(rollingvar(split_time_sc, 0, 3), 3)

Parameter: Formula7

round(rollingstdevsamp(split_time_sc, 0, 3), 3)

Parameter: Formula8

round(rollingvarsamp(split_time_sc, 0, 3), 3)

Parameter: Group by

lap

Parameter: Order by

lap

However, you must rename all of the generated windowX columns.

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