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Вернуться к Demand Forecasting Using Time Series

Отзывы учащихся о курсе Demand Forecasting Using Time Series от партнера LearnQuest

О курсе

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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1–9 из 9 отзывов о курсе Demand Forecasting Using Time Series

автор: Michail K

18 сент. 2021 г.

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.

автор: Khoa N M

5 нояб. 2021 г.

I learnt a lot from this course.

автор: Hediyeh S

11 мар. 2022 г.

I think it needs to complete more.

автор: Sebastian R

27 сент. 2021 г.

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

автор: florence b

20 сент. 2021 г.

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.

автор: Brandon B

9 мар. 2022 г.

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.

автор: irem

18 янв. 2022 г.

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.

автор: Javier A N

30 мая 2022 г.

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

автор: Serge K

7 дек. 2021 г.

Inconsistent, no feedback or answers to any questions at all