Packt – Time Series Analysis with Python 3.X-XQZT

Packt – Time Series Analysis with Python 3.X-XQZT
English | Size: 732.63 MB
Category: Tutorial

Key Features
Perform efficient time series analysis using Python and master essential machine learning models
Apply various time series methods and techniques and assemble a project step-by-step
Build a complete project on anomaly detection that has a distinct emphasis on applications in the finance (or any other) domain

What You Will Learn

Key pandas concepts and techniques for time-based analysis
Study and work with important components of time series data such as trends, seasonality, and noise
Apply commonly used machine learning models for analysis
How to de-trend and de-seasonlize time series data
Manipulate data with AR, MA, and ARMA
Decompose time series data into its components for efficient analysis
Create an end-to-end anomaly detection project based on time series

About

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.

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