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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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.
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.
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.
Inconsistent, no feedback or answers to any questions at all
the assingment have some errors in the instuctions, the objectives described are not graded correctly
Very bad course!! Double videos, bad programing labs( with wrong instructions vs expected results) not much discussion on results...just "push this button" instructions
Muy confuso con poca practica, creo que cuando el objetivo es programar es esencial tener los recursos para poder crear los códigos, .
Great course to gain fundemantals of Time Series Analyses for Demand Forecasting..
I learnt a lot from this course.
Good but lab should be improved
GOOD
I think it needs to complete more.
One question in the final project is wrong
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.
There are major mistakes with the grading system and this module doesn't let me finish the course. Terrible.