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Luis Alberto Alaniz Castillo
Create a Jupyter Notebook in order to forecast a univariate time series (in our case new one family home sales) using an LSTM. You will also be able to tell when univariate time series have the appropriate structure to be forecasted with LSTM's or even using...
Read more
Create a Jupyter Notebook in order to forecast a univariate time series (in our case new one family home sales) using an LSTM. You will also be able to tell when univariate time series have the appropriate structure to be forecasted with LSTM's or even using...
Read more
Create a Jupyter Notebook in order to forecast a univariate time series (in our case new one family home sales) using an LSTM. You will also be able to tell when univariate time series have the appropriate structure to be forecasted with LSTM's or even using any other univariate forecasting techniques.
This Guided Project was created by a Coursera community member.
Register for this course and see more details by visiting:
OpenCourser.com/course/wp4qt6/forecasting
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possible dealbreakers
Provides LSTM and univariate time series fundamentals for learners who are involved in time series analysis
Gives hands-on practice with Jupyter Notebooks for time series forecasting
Covers topics in a structured and organized way, making it easy to follow for beginners
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Reviews summary
Explanations are often inadequate
With all of the reviews landing at the extremes of 5 or below 3, it is safe to say that some students will find value in this course while others will be put off. More than anything, students felt that the course's explanations were lacking. If you are comfortable with time series analysis at the most conceptual level, and if you have experience coding in Jupyter Notebooks, this course may be a good value for you.
This instructor's language skills detract from instruction.
"His english is also not that good which makes it even more difficult"
This course focuses more on coding than conceptual understanding.
"The project's focus was more on writing code and getting it to run"
This course would be improved by more thorough explanations.
"... the instructor does not provide any explanations."
"... more explanation is required in terms of understanding time series analysis at a very conceptual level."
"explantion of thing was inadequate."
Activities
Be better prepared
before
your course. Deepen your understanding
during
and
after
it. Supplement your coursework and achieve mastery of the topics covered
in Forecasting Univariate Time Series with an LSTM with these
activities:
Review: Time Series Analysis with Applications in R
Show steps
Reviewing this book will help lay a strong foundation for understanding the techniques used in time series analysis and forecasting, including LSTM.
View
Time Series Analysis: With Applications in R...
on Amazon
Show steps
-
Read the first three chapters
-
Complete the practice exercises at the end of each chapter
-
Summarize the key concepts in each chapter
Compile a notebook of all the relevant materials for the course
Show steps
Having all of the relevant materials in one place will help you stay organized and focused.
Show steps
-
Create a new notebook for the course
-
Add copies of the syllabus, lecture notes, and assignments to your notebook
-
Include any other relevant materials, such as links to online resources or articles
Find a mentor who can provide guidance on LSTM time series forecasting
Show steps
A mentor can provide you with valuable insights and support as you learn about LSTM time series forecasting.
Show steps
-
Identify potential mentors who have experience with LSTM time series forecasting
-
Reach out to your potential mentors and ask for their guidance
-
Meet with your mentor regularly to discuss your progress and get feedback
Four other activities
Expand to see all activities and additional details
Show all seven activities
LearnLSTM.com
Show steps
LearnLSTM.com is a great resource for learning about LSTMs and how to use them for time series forecasting.
Browse courses on
Long Short-Term Memory
Show steps
-
Complete the first three tutorials
-
Try out the examples provided in the tutorials
-
Post a question on the forum if you get stuck
Mentor a fellow student in the course
Show steps
Mentoring others will help you solidify your own understanding of the material.
Show steps
-
Identify a fellow student who could benefit from your help
-
Meet with your mentee regularly to answer their questions and provide feedback
-
Encourage your mentee to participate in class discussions and ask questions
Kaggle LSTM Time Series Forecasting Competition
Show steps
This competition will provide you with hands-on experience with LSTM time series forecasting.
Browse courses on
Time Series Forecasting
Show steps
-
Download the data and familiarize yourself with it
-
Build an LSTM model and train it on the data
-
Submit your predictions and see how you compare to others
LSTM Time Series Forecasting Project
Show steps
This project will allow you to apply your LSTM time series forecasting skills to a real-world dataset.
Browse courses on
Time Series Forecasting
Show steps
-
Choose a dataset and define your forecasting problem
-
Build an LSTM model and train it on the data
-
Evaluate your model and write a report on your findings
Review: Time Series Analysis with Applications in R
Show steps
Reviewing this book will help lay a strong foundation for understanding the techniques used in time series analysis and forecasting, including LSTM.
View
Time Series Analysis: With Applications in R...
on Amazon
Show steps
Show steps
Show steps
- Read the first three chapters
- Complete the practice exercises at the end of each chapter
- Summarize the key concepts in each chapter
Compile a notebook of all the relevant materials for the course
Show steps
Having all of the relevant materials in one place will help you stay organized and focused.
Show steps
Show steps
Show steps
- Create a new notebook for the course
- Add copies of the syllabus, lecture notes, and assignments to your notebook
- Include any other relevant materials, such as links to online resources or articles
Find a mentor who can provide guidance on LSTM time series forecasting
Show steps
A mentor can provide you with valuable insights and support as you learn about LSTM time series forecasting.
Show steps
Show steps
Show steps
- Identify potential mentors who have experience with LSTM time series forecasting
- Reach out to your potential mentors and ask for their guidance
- Meet with your mentor regularly to discuss your progress and get feedback
Four other activities
Expand to see all activities and additional details
Show all seven activities
LearnLSTM.com
Show steps
LearnLSTM.com is a great resource for learning about LSTMs and how to use them for time series forecasting.
Browse courses on
Long Short-Term Memory
Show steps
Show steps
Show steps
- Complete the first three tutorials
- Try out the examples provided in the tutorials
- Post a question on the forum if you get stuck
Mentor a fellow student in the course
Show steps
Mentoring others will help you solidify your own understanding of the material.
Show steps
Show steps
Show steps
- Identify a fellow student who could benefit from your help
- Meet with your mentee regularly to answer their questions and provide feedback
- Encourage your mentee to participate in class discussions and ask questions
Kaggle LSTM Time Series Forecasting Competition
Show steps
This competition will provide you with hands-on experience with LSTM time series forecasting.
Browse courses on
Time Series Forecasting
Show steps
Show steps
Show steps
- Download the data and familiarize yourself with it
- Build an LSTM model and train it on the data
- Submit your predictions and see how you compare to others
LSTM Time Series Forecasting Project
Show steps
This project will allow you to apply your LSTM time series forecasting skills to a real-world dataset.
Browse courses on
Time Series Forecasting
Show steps
Show steps
Show steps
- Choose a dataset and define your forecasting problem
- Build an LSTM model and train it on the data
- Evaluate your model and write a report on your findings
Career center
Learners who complete Forecasting Univariate Time Series with an LSTM will develop knowledge and skills
that may be useful to these careers:
Data Analyst
Data Analyst
As a Data Analyst, you will use your skills in data analysis and time series forecasting to help businesses make informed decisions. This course on Forecasting Univariate Time Series with an LSTM can help you build a foundation in time series analysis and forecasting, which is a key skill for Data Analysts. This course covers topics such as data preprocessing, feature engineering, model selection, and evaluation, all of which are essential for success in this field.
Data Scientist
Data Scientist
Data Scientists use their skills in data analysis and machine learning to solve complex business problems. This course on Forecasting Univariate Time Series with an LSTM can help you develop the skills you need to succeed as a Data Scientist, particularly in the area of time series forecasting. The course covers topics such as data preprocessing, feature engineering, model selection, and evaluation, which are all essential for success in this field.
Machine Learning Engineer
Machine Learning Engineer
Machine Learning Engineers design and develop machine learning models to solve complex problems. This course on Forecasting Univariate Time Series with an LSTM can help you build a foundation in time series analysis and forecasting, which is a key skill for Machine Learning Engineers. The course covers topics such as data preprocessing, feature engineering, model selection, and evaluation, all of which are essential for success in this field.
Quantitative Analyst
Quantitative Analyst
Quantitative Analysts use their skills in mathematics and statistics to analyze financial data and make investment decisions. This course on Forecasting Univariate Time Series with an LSTM can help you develop the skills you need to succeed as a Quantitative Analyst, particularly in the area of time series forecasting. The course covers topics such as data preprocessing, feature engineering, model selection, and evaluation, which are all essential for success in this field.
Statistician
Statistician
Statisticians use their skills in mathematics and statistics to collect, analyze, interpret, and present data. This course on Forecasting Univariate Time Series with an LSTM can help you build a foundation in time series analysis and forecasting, which is a key skill for Statisticians. The course covers topics such as data preprocessing, feature engineering, model selection, and evaluation, all of which are essential for success in this field.
Financial Analyst
Financial Analyst
Financial Analysts use their skills in finance and economics to analyze financial data and make investment recommendations. This course on Forecasting Univariate Time Series with an LSTM can help you develop the skills you need to succeed as a Financial Analyst, particularly in the area of time series forecasting. The course covers topics such as data preprocessing, feature engineering, model selection, and evaluation, which are all essential for success in this field.
Business Analyst
Business Analyst
Business Analysts use their skills in business and data analysis to help businesses improve their performance. This course on Forecasting Univariate Time Series with an LSTM can help you develop the skills you need to succeed as a Business Analyst, particularly in the area of time series forecasting. The course covers topics such as data preprocessing, feature engineering, model selection, and evaluation, all of which are essential for success in this field.
Operations Research Analyst
Operations Research Analyst
Operations Research Analysts use their skills in mathematics and statistics to solve complex problems in business and industry. This course on Forecasting Univariate Time Series with an LSTM can help you develop the skills you need to succeed as an Operations Research Analyst, particularly in the area of time series forecasting. The course covers topics such as data preprocessing, feature engineering, model selection, and evaluation, all of which are essential for success in this field.
Actuary
Actuary
Actuaries use their skills in mathematics and statistics to assess risk and uncertainty. This course on Forecasting Univariate Time Series with an LSTM can help you develop the skills you need to succeed as an Actuary, particularly in the area of time series forecasting. The course covers topics such as data preprocessing, feature engineering, model selection, and evaluation, all of which are essential for success in this field.
Software Engineer
Software Engineer
Software Engineers design, develop, and maintain software systems. This course on Forecasting Univariate Time Series with an LSTM may be useful for Software Engineers who are interested in developing time series forecasting applications. The course covers topics such as data preprocessing, feature engineering, model selection, and evaluation, which are all essential for success in this field.
Data Engineer
Data Engineer
Data Engineers design, build, and maintain data pipelines. This course on Forecasting Univariate Time Series with an LSTM may be useful for Data Engineers who are interested in developing time series forecasting applications. The course covers topics such as data preprocessing, feature engineering, model selection, and evaluation, which are all essential for success in this field.
Database Administrator
Database Administrator
Database Administrators design, build, and maintain databases. This course on Forecasting Univariate Time Series with an LSTM may be useful for Database Administrators who are interested in developing time series forecasting applications. The course covers topics such as data preprocessing, feature engineering, model selection, and evaluation, which are all essential for success in this field.
Network Administrator
Network Administrator
Network Administrators design, build, and maintain computer networks. This course on Forecasting Univariate Time Series with an LSTM may be useful for Network Administrators who are interested in developing time series forecasting applications. The course covers topics such as data preprocessing, feature engineering, model selection, and evaluation, which are all essential for success in this field.
Systems Analyst
Systems Analyst
Systems Analysts design, build, and maintain computer systems. This course on Forecasting Univariate Time Series with an LSTM may be useful for Systems Analysts who are interested in developing time series forecasting applications. The course covers topics such as data preprocessing, feature engineering, model selection, and evaluation, which are all essential for success in this field.
Information Security Analyst
Information Security Analyst
Information Security Analysts design, build, and maintain computer security systems. This course on Forecasting Univariate Time Series with an LSTM may be useful for Information Security Analysts who are interested in developing time series forecasting applications. The course covers topics such as data preprocessing, feature engineering, model selection, and evaluation, which are all essential for success in this field.
For more career information including salaries, visit:
OpenCourser.com/course/wp4qt6/forecasting
Reading list
We've selected seven books
that we think will supplement your
learning. Use these to
develop background knowledge, enrich your coursework, and gain a
deeper understanding of the topics covered in
Forecasting Univariate Time Series with an LSTM .
Provides a comprehensive overview of deep learning, including the theory, implementation, and applications. It valuable resource for anyone who wants to learn more about deep learning, and it is particularly relevant to this course because it covers LSTM networks in detail.
Provides a comprehensive overview of Python for data analysis. It valuable resource for anyone who wants to learn more about data analysis with Python, and it is particularly relevant to this course because it covers the basics of Python programming.
Save
Provides a comprehensive overview of deep learning. It valuable resource for anyone who wants to learn more about deep learning, and it is particularly relevant to this course because it covers the basics of deep learning and LSTM networks.
Save
Provides a comprehensive overview of time series analysis and forecasting. It valuable resource for anyone who wants to learn more about time series analysis and forecasting, and it is particularly relevant to this course because it covers LSTM networks.
Save
Provides a comprehensive overview of time series analysis. It valuable resource for anyone who wants to learn more about time series analysis, and it is particularly relevant to this course because it covers LSTM networks.
Save
Provides a comprehensive overview of time series analysis and forecasting. It valuable resource for anyone who wants to learn more about time series analysis and forecasting, and it is particularly relevant to this course because it covers LSTM networks.
Provides a comprehensive overview of time series analysis using Python. It valuable resource for anyone who wants to learn more about time series analysis and Python, and it is particularly relevant to this course because it covers LSTM networks.
For more information about how these books relate to this course, visit:
OpenCourser.com/course/wp4qt6/forecasting
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Time
8 hours
Via
Coursera
Institution
Coursera Community Project Network
Instructor
Luis Alberto Alaniz Castillo
Good to know
Provides LSTM and univariate time series fundamentals for learners who are involved in time series analysis
Gives hands-on practice with Jupyter Notebooks for time series forecasting
Covers topics in a structured and organized way, making it easy to follow for beginners
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