Time Series, Path, and Attribution Analysis - Aster Analytics

Teradata Aster® Analytics Foundation User GuideUpdate 2

Aster Analytics
Release Number
September 2017
English (United States)
Last Update
Product Category
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 input to 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.
Fast Fourier Transform Functions FFT uses a Fast Fourier Transform (FFT) algorithm to compute the discrete Fourier Transform (DFT) of each signal in one or more input table columns. A signal can be either real or complex, and can have one, two, or three dimensions.

IFFT uses an inverse Fast Fourier Transform (IFFT) algorithm (also called a Fourier synthesis algorithm) to reverse the Fast Fourier Transform (FFT) performed by the FFT function.

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.
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 Turns time series into symbolic strings, which are smaller and in which patterns are more obvious. Input and output formats allow SAX2 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.
TimeSeriesOrders Determines the orders and whether to incorporate the drift term into a time series model.
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.
Wavelet Transform Functions Implement Mallat's algorithm (an iterate algorithm in the Discrete Wavelet Transform field) and apply wavelet transform on multiple sequences simultaneously, or perform the inverse of this process. The wavelet transform function are DWT, DWT2D, IDWT, and IDWT2D.