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.
Этот курс входит в специализацию ''Специализация Machine Learning for Supply Chains'
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Курс 2 из 4 в программе
Средний уровень
Basic understanding of Python, Pandas, and Numpy.
Прибл. 9 часов на выполнение
Английский
Субтитры: Английский
Чему вы научитесь
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.
Приобретаемые навыки
- Machine Learning
- Python Programming
- Autoregressive Integrated Moving Average (ARIMA)
- Time Series
- Demand Forecasting
Гибкие сроки
Назначьте сроки сдачи в соответствии со своим графиком.
Сертификат, ссылками на который можно делиться с другими людьми
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100% онлайн
Начните сейчас и учитесь по собственному графику.
Курс 2 из 4 в программе
Средний уровень
Basic understanding of Python, Pandas, and Numpy.
Прибл. 9 часов на выполнение
Английский
Субтитры: Английский
от партнера
LearnQuest
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Программа курса: что вы изучите
2 ч. на завершение
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 ч. на завершение
7 видео ((всего 29 мин.)), 3 материалов для самостоятельного изучения, 2 тестов
2 ч. на завершение
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 ч. на завершение
8 видео ((всего 36 мин.)), 2 материалов для самостоятельного изучения, 2 тестов
3 ч. на завершение
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 ч. на завершение
4 видео ((всего 18 мин.)), 1 материал для самостоятельного изучения, 2 тестов
3 ч. на завершение
Final Project
In the final course project, we'll make demand predictions using ARIMA models.
3 ч. на завершение
Специализация Machine Learning for Supply Chains: общие сведения
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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