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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À propos de ce cours
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Dates limites flexibles
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Certificat partageable
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100 % en ligne
Commencez dès maintenant et apprenez aux horaires qui vous conviennent.
Cours 2 sur 4 dans le
Niveau intermédiaire
Basic understanding of Python, Pandas, and Numpy.
Approx. 9Â heures pour terminer
Anglais
Sous-titres : Anglais
Ce que vous allez apprendre
B​uilding ARIMA models in Python to make demand predictions
D​eveloping the framework for more advanced neural netowrks (such as LSTMs) by understanding autocorrelation and autoregressive models.
Compétences que vous acquerrez
- Machine Learning
- Python Programming
- Autoregressive Integrated Moving Average (ARIMA)
- Time Series
- Demand Forecasting
Dates limites flexibles
Réinitialisez les dates limites selon votre disponibilité.
Certificat partageable
Obtenez un Certificat lorsque vous terminez
100 % en ligne
Commencez dès maintenant et apprenez aux horaires qui vous conviennent.
Cours 2 sur 4 dans le
Niveau intermédiaire
Basic understanding of Python, Pandas, and Numpy.
Approx. 9Â heures pour terminer
Anglais
Sous-titres : Anglais
Offert par
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.
Programme de cours : ce que vous apprendrez dans ce cours
2Â heures pour terminer
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.
2Â heures pour terminer
7 vidéos (Total 29 min), 3 lectures, 2 quiz
2Â heures pour terminer
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.
2Â heures pour terminer
8 vidéos (Total 36 min), 2 lectures, 2 quiz
3Â heures pour terminer
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).
3Â heures pour terminer
4 vidéos (Total 18 min), 1 lecture, 2 quiz
3Â heures pour terminer
Final Project
In the final course project, we'll make demand predictions using ARIMA models.
3Â heures pour terminer
À propos du Spécialisation Machine Learning for Supply Chains
This specialization is intended for students who wish to use machine language to analyze and predict product usage and other similar tasks. There is no specific prerequisite but some general knowledge of supply chain will be helpful, as well as general statistics and calculus.
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