Book Image

Time Series Analysis with Python Cookbook

By : Tarek A. Atwan
Book Image

Time Series Analysis with Python Cookbook

By: Tarek A. Atwan

Overview of this book

Time series data is everywhere, available at a high frequency and volume. It is complex and can contain noise, irregularities, and multiple patterns, making it crucial to be well-versed with the techniques covered in this book for data preparation, analysis, and forecasting. This book covers practical techniques for working with time series data, starting with ingesting time series data from various sources and formats, whether in private cloud storage, relational databases, non-relational databases, or specialized time series databases such as InfluxDB. Next, you’ll learn strategies for handling missing data, dealing with time zones and custom business days, and detecting anomalies using intuitive statistical methods, followed by more advanced unsupervised ML models. The book will also explore forecasting using classical statistical models such as Holt-Winters, SARIMA, and VAR. The recipes will present practical techniques for handling non-stationary data, using power transforms, ACF and PACF plots, and decomposing time series data with multiple seasonal patterns. Later, you’ll work with ML and DL models using TensorFlow and PyTorch. Finally, you’ll learn how to evaluate, compare, optimize models, and more using the recipes covered in the book.
Table of Contents (18 chapters)

Reading third-party financial data using APIs

In this recipe, you will use a very useful library, pandas-datareader, which provides remote data access so that you can extract data from multiple data sources, including Yahoo Finance, Quandl, and Alpha Vantage, to name a few. The library not only fetches the data but also returns the data as a pandas DataFrame and the index as a DatetimeIndex.

Getting ready

For this recipe, you will need to install pandas-datareader.

To install it using conda, run the following command:

>>> conda install -c anaconda pandas-datareader -y

To install it using pip, run the following command:

>>> pip install pandas-datareader 

How to do it…

In this recipe, you will use the Yahoo API to pull stock data for Microsoft and Apple. Let's get started:

  1. Let's start by importing the necessary libraries:
    import pandas as pd
    import datetime
    import matplotlib.pyplot as plt
    import pandas_datareader.data...