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Time Series Analysis with Python Cookbook

Time Series Analysis with Python Cookbook

By : Tarek A. Atwan
4.8 (11)
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Time Series Analysis with Python Cookbook

Time Series Analysis with Python Cookbook

4.8 (11)
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)
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Writing to CSV and other delimited files

In this recipe, you will export a DataFrame to a CSV file and leverage the different parameters available to use in the DataFrame.to_csv() writer function.

Getting ready

The file is provided in the GitHub repository for this book, which you can find here: https://github.com/PacktPublishing/Time-Series-Analysis-with-Python-Cookbook. The file is named movieboxoffice.csv.

To prepare, let's first read the file into a DataFrame with the following code:

import pandas as pd
from pathlib import Path
filepath = Path('../../datasets/Ch4/movieboxoffice.csv')
movies = pd.read_csv(filepath,
                 header=0,
                 parse_dates=[0],
                 index_col...

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