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Demand Forecasting Using Time Series
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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| Rating | Not enough ratings |
|---|---|
| Length | 3 weeks |
| Effort | 4 hours of study, ~ 2 hours/week |
| Starts | Jul 3 (45 weeks ago) |
| Cost | $49 |
| From | LearnQuest via Coursera |
| Instructors | Rajvir Dua, Neelesh Tiruviluamala |
| Download Videos | On all desktop and mobile devices |
| Language | English |
| Subjects | Data Science Programming |
| Tags | Data Science Data Analysis Machine Learning |
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| Rating | Not enough ratings |
|---|---|
| Length | 3 weeks |
| Effort | 4 hours of study, ~ 2 hours/week |
| Starts | Jul 3 (45 weeks ago) |
| Cost | $49 |
| From | LearnQuest via Coursera |
| Instructors | Rajvir Dua, Neelesh Tiruviluamala |
| Download Videos | On all desktop and mobile devices |
| Language | English |
| Subjects | Data Science Programming |
| Tags | Data Science Data Analysis Machine Learning |
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