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. Then, we'll spend some time analyzing correlation methods in relation to time series (autocorrelation). In the 2nd half of the course, we'll focus on methods for demand prediction using time series, such as autoregressive models. Finally, we'll conclude with a project, predicting demand using ARIMA models in Python.
Demand Forecasting Using Time Series
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Demand Forecasting Using Time Series
This course is part of Machine Learning for Supply Chains Specialization
Instructors: Rajvir Dua
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3.4
(30 reviews)
Intermediate level
Recommended experience
9 hours to complete
3 weeks at 3 hours a week
Flexible schedule
Learn at your own pace
What you'll learn
Building ARIMA models in Python to make demand predictions
Developing the framework for more advanced neural netowrks (such as LSTMs) by understanding autocorrelation and autoregressive models.
Skills you'll gain
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Assessments
5 assignments
Taught in English
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This course is part of the Machine Learning for Supply Chains Specialization
When you enroll in this course, you'll also be enrolled in this Specialization.
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There are 4 modules in this course
In this module, we'll get our feet wet with time series in Python. We'll start by getting familiar with where time series fits in to the machine learning landscape. Then, we'll learn about the main types of time series and their distinguishing factors, including period, frequency, and stationarity. After pausing to learn how to plot timeseries in Python, we'll explore the differences between seasonality and cyclicality.
What's included
7 videos3 readings2 assignments1 discussion prompt
In this module, we'll dive into the ideas behind autocorrelation and independence. We'll start by digging into the math of correlation and how it can be used to characterize the relationship between two variables. Next, we'll define its relationship to independence and explain where these ideas can be used. Finally, we'll combine correlation with time series attributes, such as trend, seasonality, and stationarity to derive autocorrelation. We'll go through both some of the theory behind autocorrelation, and how to code it in Python.
What's included
8 videos2 readings2 assignments1 discussion prompt
In this module, we'll start by reviewing some of the basic concepts behind linear regression. Then, we'll extend this knowledge to feed into lagged regression, an effective way to use regression techniques on time series. Once we have a solid foothold in basic and lagged regression, we'll explore modern methods such as ARIMA (autoregressive integrated moving average). All of this is building the framework for more advanced machine learning models such as LSTMs (long short-term memory network).
What's included
4 videos1 reading1 assignment1 programming assignment1 discussion prompt1 ungraded lab
In the final course project, we'll make demand predictions using ARIMA models.
What's included
1 programming assignment1 ungraded lab
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Rutgers the State University of New Jersey
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