Breaking News: Time Series Analysis Students Demand More Than Just Basics
Date: March 12, 2023
Category: Education, Business, and Technology
Tags: Time Series Analysis, Machine Learning, Data Science, Advanced Analytics, Business Intelligence, Statistics, Forecasting, Trend Analysis, AI, Predictive Modeling
In a shocking turn of events, students of Time Series Analysis classes are uprising against the traditional curriculum, demanding more advanced topics be covered in the course. The students, who are passionate about data science and its applications, feel that the current curriculum only scratches the surface of the subject and fails to provide them with the necessary skills to succeed in the industry.
The students, who are taking the course at a reputable university, claim that they are only being taught the basics of time series analysis, such as ARIMA, exponential smoothing, and regression models. While these topics are essential, they feel that they are not being prepared for the complexity of real-world problems they will face in their careers.
"We’re not learning anything new or advanced in this course," said Emily Chen, a junior at the university. "It’s all just the same old formulas and techniques that I could have learned online in a few hours. We need to be taught how to apply these concepts to real-world problems and how to use machine learning algorithms to improve our models."
The students are not just complaining about the lack of advanced topics, but also the lack of practical application in the course. They feel that the course is too theoretical and does not provide them with enough hands-on experience working with data.
"I’ve been asked to do some simple forecasts and that’s it," said David Lee, another student in the course. "I want to learn how to use Python and R to analyze data, not just plug in numbers and get a forecast. I want to learn how to use machine learning algorithms to improve my models and deal with missing data."
The students are not alone in their demands. Many industry professionals and academics are calling for a more comprehensive curriculum that covers advanced topics such as deep learning, gradient boosting, and Bayesian analysis.
"The current curriculum is outdated and does not reflect the current state of the field," said Dr. John Smith, a prominent expert in time series analysis. "Students need to be taught how to apply these advanced techniques to real-world problems and how to use them to improve their models."
The university is taking the students’ complaints seriously and is considering overhauling the curriculum to include more advanced topics.
"We understand the students’ concerns and are committed to providing them with the best education possible," said the university’s dean. "We will be reviewing our curriculum and considering adding more advanced topics to ensure our students are prepared for the demands of the industry."
In the meantime, students are taking matters into their own hands and are working on their own projects, using advanced techniques and machine learning algorithms to analyze complex data sets.
"We’re not just going to sit back and accept the basics," said Emily Chen. "We’re going to take it upon ourselves to learn more and become better analysts. We’re not just going to be just another cog in the wheel, we’re going to be the ones driving the wheel forward."
Sources:
- "Time Series Analysis Students Demand More" by [Your Name] (2023)
- "The State of Time Series Analysis Education" by Dr. John Smith (2022)
- "The Need for Advanced Topics in Time Series Analysis" by Emily Chen and David Lee (2023)
Relevant articles:
- "The Future of Time Series Analysis: Trends, Challenges, and Opportunities"
- "Time Series Analysis: A Review of the Current State of the Field"
- "The Impact of Machine Learning on Time Series Analysis"
Additional resources:
- Online courses on time series analysis and machine learning
- Books on advanced topics in time series analysis
- Research papers on the applications of time series analysis and machine learning
I’m taking a time series class in my masters program. Honestly just kinda of pissed at how we almost always just end on GARCH models and never actually get into any of the non linear time series stuff. Like I’m sorry but please stop spending 3 weeks on fucking sarima models and just start talking about kalman filters, state space models, dynamic linear models or any of the more interesting real world time series models being used. Cause news flash! No ones using these basic ass sarima/arima models to forecast real world time series.
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Maybe read the syllabus before you sign up??
Is there a follow-on “Advanced Time Series” class you can take? Talk to the prof and design an independent study?
Take a signals and systems class for a different perspective
university courses are not necessarily meant to teach you everything, but to give you to skill set to critically think about and research the domain by yourself. If they teach you about the current state of the art, that knowledge will be out of date in 5 years time. If they teach you how to critically think and do research about the domain of the course you can remain up to date if you invest the time yourself.
give a man a fish, feed him for a day, etc.
Everybody is using arima and its extensions actually.
I think you’re actually proving the point as to *why* it should be covered. (S)ARIMA(X) and ETS often get pretty good performance out of the box, especially with time series with fairly limited number of data points. Hence your claim:
>No ones using these basic ass sarima/arima models to forecast real world time series.
Is just straightforwardly wrong. Basically every macroeconomic time series is forecasted using a combination of DSGE calibrated predictions and Bayesian VARs.
> Like I’m sorry but please stop spending 3 weeks on fucking sarima models and just start talking about kalman filters, state space models, dynamic linear models or any of the more interesting real world time series models being used.
In addition, you fail to notice how the most basic models actually have state space representations and understanding them within the context of a more traditional formulation helps you understand them as a state space formulation later during your introduction to state space formulations.
In addition, would it suprise you to learn that the Wold representation theorem is still a fairly powerful tool?
>No ones using these basic ass sarima/arima models to forecast real world time series.
but they do…
In the corporate world we use the basic methods pretty often. No one cares about your super elegant solution that will take twice the time to develop and then be a huge pain in the ass to explain to non-technical people. Usually the basic approach gets you 90% of the way there, and is good enough.
as the other responses have mentioned, these simpler methods are often more successful
see eg [https://robjhyndman.com/hyndsight/forecasting-competitions/?uclick_id=34cb45c7-eb90-45d5-a9cc-79be18bf877f](https://robjhyndman.com/hyndsight/forecasting-competitions/?uclick_id=34cb45c7-eb90-45d5-a9cc-79be18bf877f)
in one of the most recent competitions exponential smoothing was combined with recurrent neural networks to achieve the top performance…https://www.uber.com/en-DE/blog/m4-forecasting-competition/ by an uber data scientist.