ARIMA Models in Python
Learn how to use ARIMA models in Python to analyze time series data. Understand the importance of stationarity in ARMA models, how to check for stationarity visually and using a standard statistical test, and how to fit ARMA, ARIMA, and ARMAX models using the statsmodels package. Discover a framework for organizing time series projects and learn how to fit more complex data with seasonal ARIMA models. Finally, decompose data into seasonal and non-seasonal components and apply ARIMA tools to a global forecast challenge. ▼
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Course Feature
Cost:
Free Trial
Provider:
Datacamp
Certificate:
No Information
Language:
English
Course Overview
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Updated in [June 30th, 2023]
This course provides an overview of ARIMA models in Python. Participants will learn about the most important time series properties, the importance of stationarity in ARMA models, and how to check for stationarity visually and using a standard statistical test. Participants will also learn the fundamental structure of ARMA models and how to apply it to generate ARMA data and fit an ARMA model. Additionally, participants will learn how to fit ARMA, ARIMA, and ARMAX models using the elegant statsmodels package. They will also learn how to identify promising model orders from the data itself, and then, once the most promising models have been trained, how to select the best model from this fitted selection. Furthermore, participants will learn a fantastic framework for organizing their time series projects, how to fit more complex data with seasonal ARIMA models, and how to decompose this data into seasonal and non-seasonal components, and then apply all of their ARIMA tools to one final global forecast challenge.
[Applications]
After this course, students can apply their knowledge of ARIMA models to their own time series projects. They can use the techniques learned to identify stationarity, fit ARMA, ARIMA, and ARMAX models, and select the best model from a fitted selection. Additionally, they can use the framework provided to organize their time series projects and apply seasonal ARIMA models to more complex data. Finally, they can use the techniques learned to tackle a global forecast challenge.
[Career Paths]
The career path recommended to learners of this course is a Data Scientist specializing in Time Series Analysis. A Data Scientist specializing in Time Series Analysis is responsible for analyzing and interpreting time series data to identify trends and patterns, and to develop predictive models. They use a variety of techniques, such as ARIMA models, to analyze and interpret time series data. They also develop forecasting models to predict future trends and patterns.
The development trend for this career path is increasing demand. As businesses become more data-driven, the need for data scientists who specialize in time series analysis is growing. Companies are increasingly relying on data scientists to provide insights into their data and to develop predictive models that can help them make better decisions. Additionally, the development of new technologies, such as machine learning and artificial intelligence, is creating new opportunities for data scientists to explore and develop more sophisticated models.
[Education Paths]
The recommended educational path for learners interested in ARIMA models in Python is to pursue a degree in Data Science or Statistics. This degree will provide learners with the necessary skills and knowledge to understand and apply ARIMA models in Python. The degree will cover topics such as data analysis, machine learning, data visualization, and programming. It will also provide learners with the opportunity to gain hands-on experience with ARIMA models in Python.
The development trend for this degree is to focus on the application of ARIMA models in Python to solve real-world problems. This includes the use of ARIMA models to analyze and forecast time series data, as well as the use of ARIMA models to develop predictive models. Additionally, the degree will focus on the use of ARIMA models in combination with other machine learning techniques, such as deep learning and reinforcement learning, to create more powerful predictive models.
Course Syllabus
ARMA Models
Fitting the Future
The Best of the Best Models
Seasonal ARIMA Models
Course Provider
Provider Datacamp's Stats at AZClass
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