This course is part of the Machine Learning for Supply Chains Specialization

3.3
stars
20 ratings

Rajvir Dua +1 more instructor

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Machine Learning for Supply Chains SpecializationLearnQuest

About this Course

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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.

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Shareable Certificate

Earn a Certificate upon completion

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Coursera Labs

Includes hands on learning projects.

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Course 2 of 4 in the

Machine Learning for Supply Chains Specialization

Intermediate Level

Basic understanding of Python, Pandas, and Numpy.

Approx. 9 hours to complete

English

What you will 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 will gain

Machine Learning
Python Programming
Autoregressive Integrated Moving Average (ARIMA)
Time Series
Demand Forecasting

Flexible deadlines

Reset deadlines in accordance to your schedule.

Shareable Certificate

Earn a Certificate upon completion

100% online

Start instantly and learn at your own schedule.

Coursera Labs

Includes hands on learning projects.

Learn more about Coursera Labs

Course 2 of 4 in the

Machine Learning for Supply Chains Specialization

Intermediate Level

Basic understanding of Python, Pandas, and Numpy.

Approx. 9 hours to complete

English

Instructors

Instructor rating

2.77/5 (11 Ratings)

Rajvir Dua

Head of Data Science, Machine Learning Solutions

7,961 Learners

8 Courses

Neelesh Tiruviluamala

Co-founder and CEO, Machine Learning Solutions

7,961 Learners

8 Courses

Offered by

LearnQuest

LearnQuest is the preferred training partner to the world’s leading companies, organizations, and government agencies. Our team boasts 20+ years of experience designing, developing and delivering a full suite industry-leading technology education classes and training solutions across the globe. Our trainers, equipped with expert industry experience and an unparalleled commitment to quality, facilitate classes that are offered in various delivery formats so our clients can obtain the training they need when and where they need it.

Syllabus - What you will learn from this course

Week1

Week 1

2 hours to complete

A First Glance at Time Series

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.

2 hours to complete

7 videos (Total 29 min), 3 readings, 2 quizzes

2 hours to complete

Independence and Autocorrelation

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.

2 hours to complete

8 videos (Total 36 min), 2 readings, 2 quizzes

Week2

Week 2

3 hours to complete

Regression and ARIMA Models

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).

3 hours to complete

4 videos (Total 18 min), 1 reading, 2 quizzes

3 hours to complete

Final Project

3 hours to complete

Reviews

3.3

10 reviews

5 stars
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3 stars
4.76%
2 stars
23.80%
1 star
23.80%

TOP REVIEWS FROM DEMAND FORECASTING USING TIME SERIES

by SFSep 12, 2022

Great course to gain fundemantals of Time Series Analyses for Demand Forecasting..

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About the Machine Learning for Supply Chains Specialization

Frequently Asked Questions

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Demand Forecasting Using Time Series

About this Course

What you will learn

Skills you will gain