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!

 

Student rating

5.00 Out Of 46 Students

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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.

 

What Students Are Saying

Great course, definitely worth the money (and then some...). Big fan.

Nio Fratzer

Course was very useful to comprehend the time series concepts and also helped to understand the application of concepts using python. Many datasets were used and that brings more confidence.

Anonymized User

Course content , teaching are satisfactory. However, we need to explore concepts in order to understand what is going on under the hood.nOverall satisfied with the course and teaching style.

Monesh B

I think every detail about timeseries is covered. Course comes with very detailed notebooks. I knew time series from past time and I was very pleasantly surprised.

Vitomir Jovanović

Great course, very easy to follow with some good examples. Highly recommend for those, looking to learn Time series Analysis.

Deepak yadav

Walks through everything from no python knowledge to pro

Adolfo Salinas

Final certification of my to-do list now completed before starting my new role! This course was excellent, easily my most recommendable offering to date. Thanks Jose Marcial Portilla!

Cameron Jones

Foxtel Group

Very well explained all concepts and their application on the notebook

Jose Vega

Very informative and engaging course. Great for beginners as well. Keep it up :)

Arnab Kundu

It is covering all of the tings that I didn't know or understand from other sources. The jupyter notebooks and slides are also very good resources to look back on for solving future problems.

Ernest Murray
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Course type
  • In-House
  • Self-Paced
Student rating

5.00

Course length

95 Lectures

18 Hours

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