In today's data-driven world, the ability to accurately forecast and predict future trends is crucial for businesses to stay ahead of the competition. Time series analysis is a powerful tool that allows organizations to unravel patterns and make informed decisions. This course, Time Series Mastery: Unravelling Patterns with ETS, ARIMA, and Advanced Forecasting Techniques, provides a comprehensive introduction to time series analysis and forecasting. You will learn about the most widely used techniques, including Error-Trend-Seasonality (ETS), Autoregressive Integrated Moving Average (ARIMA), and advanced forecasting methods. By the end of this course, you will have the skills and knowledge to apply these techniques to real-world data and make accurate predictions.

Time Series Mastery: Forecasting with ETS, ARIMA, Python
本课程是多个项目的一部分。


位教师:Diogo Resende
访问权限由 New York State Department of Labor 提供
2,663 人已注册
您将学到什么
Apply the most widely used techniques, including Exponential Smoothing (ETS) and Autoregressive Integrated Moving Average (ARIMA).
Analyze real-world data to identify patterns and make accurate predictions.
Create advanced forecasting models using Python.
您将获得的技能
- Predictive Modeling
- Time Series Analysis and Forecasting
- Business Analytics
- Strategic Decision-Making
- Data-Driven Decision-Making
- Advanced Analytics
- Jupyter
- Exploratory Data Analysis
- Market Data
- Pandas (Python Package)
- NumPy
- Trend Analysis
- Forecasting
- Financial Forecasting
- Statistical Analysis
- Matplotlib
- 技能部分已折叠。显示 8 项技能,共 16 项。
要了解的详细信息

添加到您的领英档案
4 项作业
了解顶级公司的员工如何掌握热门技能

积累特定领域的专业知识
- 向行业专家学习新概念
- 获得对主题或工具的基础理解
- 通过实践项目培养工作相关技能
- 获得可共享的职业证书

该课程共有1个模块
In today's data-driven world, the ability to accurately forecast and predict future trends is crucial for businesses to stay ahead of the competition. Time series analysis is a powerful tool that allows organizations to unravel patterns and make informed decisions. This course provides a comprehensive introduction to time series analysis and forecasting. You will learn about the most widely used techniques, including Error-Trend-Seasonality (ETS), Autoregressive Integrated Moving Average (ARIMA), and advanced forecasting methods.
涵盖的内容
16个视频4篇阅读材料4个作业
获得职业证书
将此证书添加到您的 LinkedIn 个人资料、简历或履历中。在社交媒体和绩效考核中分享。
提供方
人们为什么选择 Coursera 来帮助自己实现职业发展

Felipe M.

Jennifer J.

Larry W.

Chaitanya A.
学生评论
- 5 stars
56.81%
- 4 stars
13.63%
- 3 stars
11.36%
- 2 stars
11.36%
- 1 star
6.81%
显示 3/44 个
已于 Jun 12, 2024审阅
I think it was too basic, it lacks more a deeper dive into theoretical aspects and importance about the different scores that the summary of the model provides. However it's a good introduction
已于 Oct 16, 2024审阅
Best explanation of the key concepts in short time. Well done.






