Chevron Left
Volver a Demand Forecasting Using Time Series

Opiniones y comentarios de aprendices correspondientes a Demand Forecasting Using Time Series por parte de LearnQuest

Acerca del Curso

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....
Filtrar por:

1 - 9 de 9 revisiones para Demand Forecasting Using Time Series

por Michail K

18 de sep. de 2021

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.

por Khoa N M

5 de nov. de 2021

I learnt a lot from this course.

por Hediyeh S

11 de mar. de 2022

I think it needs to complete more.

por Sebastian R

27 de sep. de 2021

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

por florence b

20 de sep. de 2021

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.

por Brandon B

9 de mar. de 2022

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.

por irem

18 de ene. de 2022

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.

por Javier A N

30 de may. de 2022

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

por Serge K

7 de dic. de 2021

Inconsistent, no feedback or answers to any questions at all