There is a new version of this course. Please see Time Series Feature Mining and Creation.

In this course, you learn about data exploration, feature creation, and feature selection for time sequences. The topics discussed include binning, smoothing, transformations, and data set operations for time series, spectral analysis, singular spectrum analysis, distance measures, and motif analysis.

Learn how to

• Create time series data from transactional or timestamped data.
• Decompose time series into components of systematic variation.
• Perform spectral analysis to identify cycles in the data.
• Calculate distance measures between series to mitigate collinearity and rank-order features.
• Perform a motif analysis to identify repeating sub-sequences in series.

Who should attend

Analysts with a quantitative background as well as domain experts who would like to augment their time series toolbox

Formats available	Standard duration
e-Learning:	14 hours/180 day license	System Requirements

Before taking this course, you should be comfortable with basic statistical concepts. You can gain this experience by completing the Statistics 1: Introduction to ANOVA, Regression, and Logistic Regression course. Familiarity with matrices and principal component analysis is also helpful but not required.

This course addresses SAS/ETS, SAS Studio, SAS Visual Forecasting software.

Time Series Basics

• Time series basics.
• Accumulation, exploration, and binning.
• Signal.

Distance Measures

• Sequence distance basics.
• Symbolic representation of sequences.

Spectral Analysis

• Spectral analysis basics.
• The periodogram and spectral density.
• Singular spectrum analysis.

Motif Analysis

• Motif analysis basics.