Time-Series-Analysis-with-Python-3.x

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Time-Series-Analysis-with-Python-3.x

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Time Series Analysis with Python 3.x [Video], published by Packt

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# Time-Series-Analysis-with-Python-3.x [Video]
This is the code repository for [Time-Series-Analysis-with-Python-3.x [Video]](https://www.packtpub.com/data/time-series-analysis-with-python-3-x-video), published by [Packt](https://www.packtpub.com/?utm_source=github). It contains all the supporting project files necessary to work through the video course from start to finish.
## About the Video Course
Time series analysis encompasses methods for examining time series data found in a wide variety of domains. Being equipped to work with time-series data is a crucial skill for data scientists. In this course, you'll learn to extract and visualize meaningful statistics from time series data. You'll apply several analysis methods to your project. Along the way, you'll learn to explore, analyze, and predict time series data.

You'll start by working with pandas' datetime and finding useful ways to extract data. Then you'll be introduced to correlation/autocorrelation time-series relationships and detecting anomalies. You'll learn about autoregressive (AR) models and Moving Average (MA) models for time series, and explore anomalies in detail. You'll also discover how to blend AR and MA models to build a robust ARMA model. You'll also grasp how to build time series forecasting models using ARIMA. Finally, you'll complete your own project on time series anomaly detection.

By the end of this practical tutorial, you'll have acquired the skills you need to perform time series analysis using Python.

Please note that this course assumes some prior knowledge of Python programming; a working knowledge of pandas and NumPy; and some experience working with data.
<H2>What You Will Learn</H2>
<DIV class=book-info-will-learn-text>
<UL>
<LI>Key pandas concepts and techniques for time-based analysis
<LI>Study and work with important components of time series data such as trends, seasonality, and noise
<LI>Apply commonly used machine learning models for analysis
<LI>How to de-trend and de-seasonlize time series data
<LI>Manipulate data with AR, MA, and ARMA
<LI>Decompose time series data into its components for efficient analysis
<LI>Create an end-to-end anomaly detection project based on time series
  </LI></UL></DIV>

### Technical Requirements
For successful completion of this course, students will require the computer systems with at least the following:<br/>
● A computer with an internet connection (Windows, macOS, Linux with 2-4 CPU, 4-8 GB RAM)
<br/>

### Recommended Software Requirements:<br/>
For an optimal experience with hands-on labs and other practical activities, we recommend the following configuration:
<br/>
●	The latest Anaconda package 2019.07 or later<br/>
●	You can download the latest release of Anaconda:  https://www.anaconda.com/distribution/

## Related Products
* [Data Visualization Recipes in Python [Video]](https://www.packtpub.com/big-data-and-business-intelligence/data-visualization-recipes-python-video)

* [Practical Time Series Analysis [Video]](https://www.packtpub.com/big-data-and-business-intelligence/practical-time-series-analysis-video)

* [Jupyter In Depth [Video]](https://www.packtpub.com/big-data-and-business-intelligence/jupyter-depth-video)

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