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所在平台: Coursera |
课程主页: https://www.coursera.org/learn/demand-prediction-using-time-series
课程评论：没有评论
课程名称：基于时间序列的需求预测 课程概述：这是“供应链基础机器学习”系列中的第二门课程。此课程将全面探讨时间序列在需求预测中的应用。我们将从基本概念入手，了解时间序列的特性，包括平稳性、趋势（漂移）、周期性和季节性。接着，我们将分析与时间序列相关的相关性方法（自相关）。在课程的后半部分，我们将重点学习使用时间序列进行需求预测的方法，例如自回归模型。最后，我们将通过项目实践，使用ARIMA模型在Python中进行需求预测。 课程大纲： 1. 时间序列初探：我们将初步了解Python中的时间序列，识别其在机器学习中的位置，学习时间序列的主要类型及其特征，包括周期、频率和平稳性等，并通过绘制时间序列图来探讨季节性与周期性的区别。 2. 独立性与自相关：深入探讨自相关与独立性的概念，理解相关性的数学原理及其在两个变量间关系中的应用，同时将相关性与时间序列属性（如趋势、季节性和平稳性）结合，学习自相关的理论和Python编码实现。 3. 回归与ARIMA模型：复习线性回归的基本概念，并延伸到滞后回归，这是一种将回归技术应用于时间序列的有效方法。我们将在掌握基本和滞后回归的基础上，探讨现代方法如ARIMA（自回归积分滑动平均）模型。所有这一切都是为更高级的机器学习模型（如长短期记忆网络LSTM）打基础。 4. 最终项目：在课程的最终项目中，我们将利用ARIMA模型进行需求预测。
Name:A First Glance at Time Series
Description: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.
Name:Independence and Autocorrelation
Description: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.
Name:Regression and ARIMA Models
Description: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).
Name:Final Project
Description:In the final course project, we'll make demand predictions using ARIMA models.
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.
