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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Machine Learning for Supply Chains 专项课程LearnQuest课程信息
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第 2 门课程（共 4 门）
中级
Basic understanding of Python, Pandas, and Numpy.
完成时间大约为9 小时
英语（English）
字幕：英语（English）
您将学到的内容有
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.
您将获得的技能
- Machine Learning
- Python Programming
- Autoregressive Integrated Moving Average (ARIMA)
- Time Series
- Demand Forecasting
可灵活调整截止日期
根据您的日程表重置截止日期。
可分享的证书
完成后获得证书
100% 在线
立即开始，按照自己的计划学习。
第 2 门课程（共 4 门）
中级
Basic understanding of Python, Pandas, and Numpy.
完成时间大约为9 小时
英语（English）
字幕：英语（English）
提供方
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.
授课大纲 - 您将从这门课程中学到什么
完成时间为 2 小时
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.
完成时间为 2 小时
7 个视频 （总计 29 分钟）, 3 个阅读材料, 2 个测验
完成时间为 2 小时
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.
完成时间为 2 小时
8 个视频 （总计 36 分钟）, 2 个阅读材料, 2 个测验
完成时间为 3 小时
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).
完成时间为 3 小时
4 个视频 （总计 18 分钟）, 1 个阅读材料, 2 个测验
完成时间为 3 小时
Final Project
In the final course project, we'll make demand predictions using ARIMA models.
完成时间为 3 小时
关于 Machine Learning for Supply Chains 专项课程
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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