Time Series Forecasting with Python

Price
$1,895.00 USD

Duration
3 Days

 

Delivery Methods
Virtual Instructor Led
Private Group

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Course Overview

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.

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This course includes 6-months access to the full course content in on-demand format to support post-class reference and review.

Who Should Attend?

Intermediate Python developers who want to learn how to use Python to forecast time series data with a variety of methods.
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  • Interactive classroom participation: Our virtual training includes live lectures, demonstrations and virtual labs that allow you to participate in discussions with your instructor and fellow classmates to get real-time feedback.
  • Post Class Resources: Review your class content, catch up on any material you may have missed or perfect your new skills with access to resources after your course is complete.
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Course Prerequisites

Introduction to Python

Agenda

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
 

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