Time Series, Path, and Attribution Analysis

Time Series, Path, and Attribution Analysis - Aster Analytics

Teradata Aster Analytics Foundation User Guide

Product

Aster Analytics

Release Number

6.21

Published

November 2016

Language

English (United States)

Last Update

2018-04-14

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kiu1466024880662.ditamap

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B700-1021

lifecycle

Product Category

Software

Time Series, Path, and Attribution Analysis Functions
Function	Description
Arima	Calculates the coefficients for a sequence of parameters, producing an ARIMA model.
ArimaPredictor	Takes as input the ARIMA model produced by the Arima function and predicts a specified number of future values (time point forecasts) for the modeled sequence.
Attribution	Calculates attributions with a wide range of distribution models. Often used in web-page analysis.
Burst	Bursts (splits) a time interval into a series of shorter "burst" intervals that can be analyzed independently.
Change-Point Detection Functions	Detect the change points in a stochastic process or time series. The change-point detection functions are ChangePointDetection and RtChangePointDetection.
Convergent Cross-Mapping	Includes the CCMPrepare function, which adds a new partition column and partitions the data to prepare it for use with the CCM function, which tests multiple causes and effects simultaneously, reporting an effect size for each cause-effect pair.
DTW	Computes the dynamic time warping—the similarity between two sequences that vary in time or speed.
DWT	Implements Mallat’s algorithm, an iterative algorithm in the discrete wavelet transform field that applies wavelet transform on multiple sequences simultaneously.
DWT2D	Implements wavelet transforms on two-dimensional input, and simultaneously applies the transforms on multiple sequences.
FrequentPaths	Mines (finds) patterns that appear more than a specified number of times in the sequence database. The difference between sequential pattern mining and frequent pattern mining is that the former works on time sequences where the order of items must be kept.
IDWT	Applies inverse wavelet transformation on multiple sequences simultaneously. IDWT is the inverse of DWT.
IDWT2D	Simultaneously applies inverse wavelet transforms on multiple sequences. Inverse function of DWT2D.
Interpolator	Calculates missing values in a time series, using either interpolation or aggregation. Interpolation estimates missing values between known values. Aggregation combines known values to produce an aggregate value.
Path Analysis Functions	Automate path analysis. These functions are useful for clickstream analysis of web site traffic and other sequence/path analysis tasks, such as advertisement or referral attribution. The path analysis functions are Path_Generator, Path_Summarizer, Path_Start, and Path_Analyzer.
SAX2	Transforms original time series data into symbolic strings, which are more suitable for many additional types of manipulation, because of their smaller size and the relative ease with which patterns can be identified and compared. Input and output formats allow it to supply data to the Shapelet functions.
SeriesSplitter	Splits a partition into subpartitions (called splits) by creating an additional column that contains the split identifier. Optionally, the function also copies a specified number of boundary rows to each split.
Sessionize	Maps each click in a clickstream to a unique session identifier.
Shapelet Functions	Detect distinguishing features among ordered sequences (time series) and use them to cluster or classify new data. The shapelet functions are UnsupervisedShapelet, SupervisedShapeletTrainer, and SupervisedShapeletClassifier.
VARMAX	Extends the ARMA/ARIMA model to work with time series with multiple response variables (vector time series), as well as exogenous variables, or variables that are independent of the other variables in the system.