TD_​GENSERIES4SINUSOIDS

In building statistical ARMA-style forecasting models, a necessary precondition is that the time series being modeled be stationary: stationary with respect to mean; stationary with respect to covariance; and, stationary with respect to variance. Quite often, the time series that the data scientist wishes to model contains a trend - meaning that the time series is non-stationary with respect to the mean; or, alternatively may contain some periodicities (cyclic variance in data). This trend and/or periodicities must be removed before modeling may begin. . Once the data scientist has devised a formula to represent the trend or periodic behaviors, their next task is to generate a series using that formula; such that they can then input both the original series and formula driven series through a pointwise subtractor function, which forms a new series with the trend and/or periodicities subtracted out.

Options

OutputFormat

Specifies the INDEX_STYLE of the output format.

PERIODICITIES (numbers seperated by new line)

A list containing one or more floating point values; representing significant periodicities: w_1, w_2, ..., w_d. ; in terms of w_k. Where w_k = 2pi / P; P = 2pi / w_k

Output Schema

Output Schema, if Volatile is true then use user login as the schema.

Output Table

Output Table

VAL Location

VAL Location

Volatile

Specifies whether the table should be a VOLATILE table. If true, then the table is automatically deleted, otherwise it is users responsibility to remove or clean it up for space.

Nodes

• String Configuration4 ×
• Python Script2 ×
• Boolean Configuration1 ×
• Component Input1 ×
• Component Output1 ×
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Extensions

• FeatureKNIME Base nodes
• FeatureKNIME Database
• FeatureKNIME Quick Forms

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