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Demand Forecasting Using Time Series に戻る

LearnQuest による Demand Forecasting Using Time Series の受講者のレビューおよびフィードバック


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....

Demand Forecasting Using Time Series: 1 - 4 / 4 レビュー

by Michail K


Completely frustrated. They do not let the students know where the dataframes are, in order to be able to practice along the course. I searched on the course forum and there were other students asking the same questions. Where are the dataframes to practice?? No answer from anyone. I feel that I wasted my time.

by Khoa N M


I learnt a lot from this course.

by Sebastian


the assingment have some errors in the instuctions, the objectives described are not graded correctly

by florence b


Nice tutorials for an introduction but absence of statistical tests to assess the characteristics of the time series at hands. Be careful in the assignments (one test set before the lesson on ARIMA for example). There are typos in the task description from the final assignment which can be misleading and very frustrating by dealing with the automatic script correction.