Coursera

Creating Features for Time Series Data (Coursera)

Offered by SAS,
Creating Features for Time Series Data (Coursera)

This course focuses on 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.

Class Deals by MOOC List - Click here and see Coursera's Active Discounts, Deals, and Promo Codes.

In this course you learn to perform motif analysis and implement analyses in the spectral or frequency domain. You also discover how distance measures work, implement applications, explore signal components, and create time series features.
This course is appropriate for analysts with a quantitative background as well as domain experts who would like to augment their time-series tool box. Before taking this course, you should be comfortable with basic statistical concepts. You can gain this experience by completing the Statistics with SAS course. Familiarity with matrices and principal component analysis are also helpful but not required.
Course 1 of 3 in the Analyzing Time Series and Sequential Data Specialization.

Syllabus

WEEK 1
Specialization Overview
In this module you get an overview of the courses in this specialization and what you can expect.

WEEK 2
Course Overview
In this module you learn about the scope of this course and you access the software and files you will use for practices in the course.

WEEK 3
Time Series Basics
In this module, you learn about converting transactional sequences to time series. Other topics include exploring signal components in time series via decompositions and binning, and creating new time series features.

WEEK 4
Distance Measures
In this module you learn about the usefulness of distance or similarity measures between time series. Calculated distance measure are used as the basis in two analyses.

WEEK 5
Spectral Analysis and Singular Spectrum Analysis (SSA)
In this module, we discuss and illustrate the basic ideas and applications in frequency domain analysis. We also discuss SSA and present demonstrations of applied SSA.

WEEK 6
Motif Analysis
In this module you learn about detecting motifs in times series and their usefulness.

WEEK 7
Course Review

Go to Class
MOOC List is learner-supported. When you buy through links on our site, we may earn an affiliate commission.

Related Courses

Time series forecasting with Prophet (Coursera) Coursera
Coursera Project Network

Time series forecasting with Prophet (Coursera)

Data Science

Time series forecasting is a common data science task that helps organizations with resource allocation, demand planning and strategy management. In this project, you'll get hands-on experience with Facebook's open source library Prophet and you will be equipped with the knowledge to carry out fast, interpretable and reliable forecasts of business time series.

Mar 14th 2022
Self-Paced
Python Forecasting Python Programming
Details
Introduction to Genomic Technologies (Coursera) Coursera
Johns Hopkins University

Introduction to Genomic Technologies (Coursera)

Statistics & Data Analysis Data Science

This course introduces you to the basic biology of modern genomics and the experimental tools that we use to measure it. We'll introduce the Central Dogma of Molecular Biology and cover how next-generation sequencing can be used to measure DNA, RNA, and epigenetic patterns. You'll also get an introduction to the key concepts in computing and data science that you'll need to understand how data from next-generation sequencing experiments are generated and analyzed.

Nov 9th 2026
4 Weeks
Biology DNA RNA
Details
Predictive Analytics for Business Planning: Time-Series Forecasting (edX) EdX
IE University

Predictive Analytics for Business Planning: Time-Series Forecasting (edX)

Statistics & Data Analysis

Explore methods of estimating future business outputs based on historical data and gain a holistic view of using predictive analytics in business with IE University. This course aims to provide you with a robust understanding of forecasting and predictive analytics. You will gain the knowledge and skills necessary to apply these techniques in real-world scenarios, while identifying the challenges of changing processes and supporting stakeholders during transitions.

Self Paced
Self-Paced
Forecasting Business Planning Predictive Analytics
Details
Macroeconometric Forecasting (edX) EdX
International Monetary Fund - IMF,IMFx

Macroeconometric Forecasting (edX)

Economics & Finance

Learn how to create and assess forecasting models to predict macroeconomic variables such as inflation and economic growth. In this macroeconomics course, you will learn to predict macroeconomic variables such as inflation, growth or consumption, and to create statistical models in economics and use them to predict responses to economic policy.

Self Paced
Self-Paced
Macroeconomics Economics Forecasting
Details
Time Series Forecasting (Udacity) Udacity
Udacity

Time Series Forecasting (Udacity)

Business

Use models to forecast the future. The Time Series Forecasting course provides students with the foundational knowledge to build and apply time series forecasting models in a variety of business contexts. In this course, you’ll learn how to use advanced forecasting models to predict the future. You'll learn this through improving your fluency in Alteryx, a data analytics tool that enables you prepare, blend, and analyze data quickly.

Self Paced
Self-Paced
Forecasting Time Series Time Series Analysis
Details
Advanced Data Mining with Weka (FutureLearn) FutureLearn
University of Waikato

Advanced Data Mining with Weka (FutureLearn)

Data Science

Learn how to use popular packages that extend Weka's functionality and areas of application. Use them to mine your own data! This course will bring you to the wizard level of skill in data mining, following on from Data Mining with Weka and More Data Mining with Weka, by showing how to use popular packages that extend Weka’s functionality.

Available now
5-12 Weeks
Python Data Mining Weka
Details
Modeling Time Series and Sequential Data (Coursera) Coursera
SAS

Modeling Time Series and Sequential Data (Coursera)

Statistics & Data Analysis Data Science

In this course you learn to build, refine, extrapolate, and, in some cases, interpret models designed for a single, sequential series. There are three modeling approaches presented. The traditional, Box-Jenkins approach for modeling time series is covered in the first part of the course. This presentation moves students from models for stationary data, or ARMA, to models for trend and seasonality, ARIMA, and concludes with information about specifying transfer function components in an ARIMAX, or time series regression, model. A Bayesian approach to modeling time series is considered next.

Oct 26th 2026
5-12 Weeks
Modeling Data Analysis Bayesian Methods
Details
Capstone Project: Predicting Safety Stock (Coursera) Coursera
LearnQuest

Capstone Project: Predicting Safety Stock (Coursera)

Data Science

In this course, we'll make predictions on product usage and calculate optimal safety stock storage. We'll start with a time series of shoe sales across multiple stores on three different continents. To begin, we'll look for unique insights and other interesting things we can find in the data by performing groupings and comparing products within each store.

Oct 19th 2026
3 Weeks
Machine Learning Data Analysis Exploratory Data Analysis
Details