Time Series

Learn the mathematical foundations essential for financial engineering and quantitative finance: linear algebra, optimization, probability, stochastic processes, statistics, and applied computational techniques in R. Modern finance is the science of decision making in an uncertain world, and its language is mathematics. As part of the MicroMasters® Program in Finance, [...]

This course for practicing and aspiring data scientists and statisticians. It is the fourth of a four-course sequence introducing the fundamentals of Bayesian statistics. It builds on the course Bayesian Statistics: From Concept to Data Analysis, Techniques and Models, and Mixture models.

This course is the second in a specialization for Machine Learning for Supply Chain Fundamentals. In this course, we explore all aspects of time series, especially for demand prediction. We'll start by gaining a foothold in the basic concepts surrounding time series, including stationarity, trend (drift), cyclicality, and seasonality. [...]

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 [...]

This course explores different time series business forecasting methods. The course covers a variety of business forecasting methods for different types of components present in time series data — level, trending, and seasonal. We will learn about the theoretical methods and apply these methods to business data using Microsoft [...]

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 [...]

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.