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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

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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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Demand Forecasting Using Time Series: 1 - 9 / 9 レビュー

by Michail K

2021年9月18日

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

2021年11月5日

I learnt a lot from this course.

by Hediyeh S

2022年3月11日

I think it needs to complete more.

by Sebastian R

2021年9月27日

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

by florence b

2021年9月20日

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.

by Brandon B

2022年3月9日

I took this course to learn ARIMA; however the instructor doesn't cover how the model works or how the hyperparameters affect it. They only talk about autoregression, not the integration or moving average comonents. Also the Jupyter notebooks that are used during the lecture are not available for download.

by irem

2022年1月18日

The assignments are not clear and misleading. It asks an autocorrelation with a lag of 20, but the correct answer is the autocorrelation with a lag of 10. Also same video is uploaded in week 1 and week 2.

by Javier A N

2022年5月30日

Muy confuso con poca practica, creo que cuando el objetivo es programar es esencial tener los recursos para poder crear los códigos, .

by Serge K

2021年12月7日

Inconsistent, no feedback or answers to any questions at all