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Time Series Forecasting with Python
In this course, we’ll teach you how to use various Python libraries for Time Series Forecasting and Analysis to predict future data points. Enroll today!
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written by Pierian Training founder Jose Portilla!
What You’ll Learn
- Pandas for Data Manipulation
- NumPy and Python for Numerical Processing
- Pandas for Data Visualization
- How to Work with Time Series Data with Pandas
- Use Statsmodels to Analyze Time Series Data
- Use Facebook's Prophet Library for forecasting
- Understand advanced ARIMA models for Forecasting
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Course Content
This course will teach you everything you need to know to use Python for forecasting time series data to predict new future data points. You’ll review the basics on how to work with and manipulate data using the NumPy and Pandas libraries with Python. Then you’ll dive deeper into visualizations with the Pandas library, learn about the statsmodels library and its powerful built in Time Series Analysis Tools including the Error-Trend-Seasonality decomposition and basic Holt-Winters methods. The course will cover general forecasting models and teach you how to create AutoCorrelation and Partial AutoCorrelation charts with powerful ARIMA based models, including Seasonal ARIMA models and SARIMAX to include Exogenous data points. Deep Learning techniques with Recurrent Neural Networks and Facebook’s Prophet library, are also covered in this course.
- NumPy
- Pandas
- Data Visualization with Pandas
- Time Series with Pandas
- Time Series Analysis with Statsmodels
- Time Series Decomposition
- Holt-Winters Method
- Statsmodels for Forecasting
- ARIMA Forecasting
- SARIMAX Forecasting
- Vector Autoregression
- Deep Learning for Forecasting
- Tensorflow
- Recurrent Neural Networks
- Facebook’s Prophet Library
- Trends and Seasonality
Intermediate Python developers who want to learn how to use Python to forecast time series data with a variety of methods.
Course Description
This training is designed to provide students who are familiar with Python both the theory and practical toolkit to perform accurate time series analysis and forecasting. The course content begins with overviews of key python data libraries, including NumPy and Pandas . Afterwards students will learn about the statsmodels library and its powerful built in Time Series Analysis Tools. Including learning about Error-Trend-Seasonality decomposition and basic Holt-Winters methods. Students will then learn about creating AutoCorrelation and Partial AutoCorrelation charts and using them in conjunction with powerful ARIMA based models, including Seasonal ARIMA models and SARIMAX to include Exogenous data points. The course also covers state of the art Deep Learning techniques with Recurrent Neural Networks that use deep learning to forecast future data points. The training ends with Facebook’s Prophet library, a simple to use, yet powerful Python library developed to forecast into the future with time series data.
Instructor-Led
Learn directly from our expert instructors via live webinar. Also includes the full video-on-demand course.
On-Demand
Get access to this self-paced Time Series Forecasting with Python training course plus the entire Pierian on-demand Data Science Training Library with a Data Science Hero membership.
Starting at
$99
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