LearnQuest
Demand Forecasting Using Time Series
LearnQuest

Demand Forecasting Using Time Series

This course is part of Machine Learning for Supply Chains Specialization

Taught in English

Some content may not be translated

Rajvir Dua
Neelesh Tiruviluamala

Instructors: Rajvir Dua

2,926 already enrolled

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Course

Gain insight into a topic and learn the fundamentals

3.3

(24 reviews)

Intermediate level

Recommended experience

9 hours (approximately)
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.

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Assessments

5 quizzes

Course

Gain insight into a topic and learn the fundamentals

3.3

(24 reviews)

Intermediate level

Recommended experience

9 hours (approximately)
Flexible schedule
Learn at your own pace

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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 quizzes1 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 quizzes1 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 quiz1 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

Instructors

Instructor ratings
3.0 (13 ratings)
Rajvir Dua
LearnQuest
8 Courses12,670 learners

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LearnQuest

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3.3

24 reviews

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5

Reviewed on Sep 12, 2022

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