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Atwan, Tarek A. TIME SERIES ANALYSIS WITH PYTHON COOKBOOK [[electronic resource]]: practical recipes for exploratory data analysis, data preparation, forecasting, and model evaluation / Tarek A. Atwan. — [S.l.]: PACKT PUBLISHING LIMITED, 2022. — 1 online resource — <URL:http://elib.fa.ru/ebsco/3309366.pdf>.Record create date: 7/1/2022 Subject: Machine learning.; Python (Computer program language); Machine learning.; Python (Computer program language) Collections: EBSCO Allowed Actions: –
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Perform time series analysis and forecasting confidently with this Python code bank and reference manual Key Features Explore forecasting and anomaly detection techniques using statistical, machine learning, and deep learning algorithms Learn different techniques for evaluating, diagnosing, and optimizing your models Work with a variety of complex data with trends, multiple seasonal patterns, and irregularities Book Description 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. What you will learn Understand what makes time series data different from other data Apply various imputation and interpolation strategies for missing data Implement different models for univariate and multivariate time series Use different deep learning libraries such as TensorFlow, Keras, and PyTorch Plot interactive time series visualizations using hvPlot Explore state-space models and the unobserved components model (UCM) Detect anomalies using statistical and machine learning methods Forecast complex time series with multiple seasonal patterns Who this book is for This book is for data analysts, business analysts, data scientists, data engineers, or Python developers who want practical Python recipes for time series analysis and forecasting techniques. Fundamental knowledge of Python programming is required. Although having a basic math and statistics background will be beneficial, it is not necessary. Prior experience working with time series data to solve business problems will also help you to better utilize and apply the different recipes in this book.
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Table of Contents
- Cover
- Title Page
- Copyright and Credits
- Contributors
- Table of Contents
- Preface
- Chapter 1: Getting Started with Time Series Analysis
- Technical requirements
- Development environment setup
- Getting ready
- How to do it…
- How it works…
- There's more…
- See also
- Installing Python libraries
- Getting ready
- How to do it…
- How it works…
- There's more…
- See also
- Installing JupyterLab and JupyterLab extensions
- Getting ready
- How to do it…
- How it works…
- There's more…
- See also
- Chapter 2: Reading Time Series Data from Files
- Technical requirements
- Reading data from CSVs and other
delimited files
- Getting ready
- How to do it…
- How it works…
- There's more…
- See also
- Reading data from an Excel file
- Getting ready
- How to do it…
- How it works…
- There's more…
- See also
- Reading data from URLs
- Getting ready
- How to do it…
- How it works…
- There's more…
- See also
- Reading data from a SAS dataset
- Getting ready
- How to do it…
- How it works…
- There's more…
- See also
- Chapter 3: Reading Time Series Data from Databases
- Technical requirements
- Reading data from a relational database
- Getting ready
- How to do it…
- How it works…
- There's more…
- See also
- Reading data from Snowflake
- Getting ready
- How to do it...
- How it works...
- There's more...
- See also
- Reading data from a document database (MongoDB)
- Getting ready
- How to do it…
- How it works…
- There's more…
- See also
- Reading third-party financial data using APIs
- Getting ready
- How to do it…
- How it works…
- There's more…
- See also
- Reading data from a time series database (InfluxDB)
- Getting ready
- How to do it…
- How it works…
- There's more…
- See also
- Chapter 4: Persisting Time Series Data to Files
- Technical requirements
- Serializing time series data with pickle
- Getting ready
- How to do it…
- How it works…
- There's more…
- See also
- Writing to CSV and other delimited files
- Getting ready
- How to do it…
- How it works…
- There's more…
- See also
- Writing data to an Excel file
- Getting ready
- How to do it…
- How it works…
- There's more…
- See also
- Storing data to S3
- Getting ready
- How to do it…
- How it works…
- There's more…
- See also
- Chapter 6: Persisting Time Series Data to Databases
- Technical requirements
- Writing time series data to a relational database (PostgreSQL and MySQL)
- Getting ready
- How to do it…
- How it works…
- There's more…
- See also
- Writing time series data to MongoDB
- Getting ready
- How to do it…
- How it works…
- There's more…
- See also
- Writing time series data to InfluxDB
- Getting ready
- How to do it…
- How it works…
- There's more…
- See also
- Writing time series data to Snowflake
- Getting ready
- How to do it...
- How it works...
- There's more...
- See also
- Chapter 6: Working with Date and Time in Python
- Technical requirements
- Working with DatetimeIndex
- How to do it…
- How it works…
- There's more…
- See also
- Providing a format argument to DateTime
- How to do it…
- How it works…
- There's more…
- See also
- Working with Unix epoch timestamps
- How to do it…
- How it works…
- There's more…
- See also
- Working with time deltas
- How to do it…
- How it works…
- There's more…
- See also
- Converting DateTime with time zone information
- How to do it…
- How it works…
- There's more…
- See also
- Working with date offsets
- How to do it…
- How it works…
- There's more…
- See also
- Working with custom business days
- How to do it…
- How it works…
- There's more…
- See also
- Chapter 7: Handling Missing Data
- Technical requirements
- Function 1 – read_datasets
- Function 2 – plot_dfs
- Function 3 – rmse_score
- Understanding missing data
- Performing data quality checks
- Getting ready
- How to do it…
- How it works…
- There's more…
- See also
- Handling missing data with univariate imputation using pandas
- Getting ready
- How to do it…
- How it works…
- There's more…
- See also
- Handling missing data with univariate imputation using scikit-learn
- Getting ready
- How to do it…
- How it works…
- There's more…
- See also
- Handling missing data with multivariate imputation
- Getting ready
- How to do it…
- How it works…
- There's more…
- See also
- Handling missing data with interpolation
- Getting ready
- How to do it…
- How it works…
- There's more…
- See also
- Technical requirements
- Chapter 8: Outlier Detection Using Statistical Methods
- Technical requirements
- Understanding outliers
- Resampling time series data
- How to do it…
- How it works…
- There's more...
- See also
- Detecting outliers using visualizations
- Getting ready
- How to do it...
- How it works...
- There's more...
- See also
- Detecting outliers using the Tukey method
- How to do it...
- How it works...
- There's more...
- See also
- Detecting outliers using a z-score
- How to do it...
- How it works...
- There's more...
- See also
- Detecting outliers using a modified z-score
- How to do it...
- How it works...
- There's more...
- See also
- Chapter 9: Exploratory Data Analysis and Diagnosis
- Technical requirements
- Plotting time series data using pandas
- Getting ready
- How to do it…
- How it works…
- There's more…
- See also
- Plotting time series data with interactive visualizations using hvPlot
- Getting ready
- How to do it…
- How it works…
- There's more…
- See also
- Decomposing time series data
- Getting ready
- How to do it…
- How it works…
- There's more…
- See also
- Detecting time series stationarity
- Getting ready
- How to do it…
- How it works…
- There's more…
- See also
- Applying power transformations
- Getting ready
- How to do it…
- How it works…
- There's more...
- See also
- Testing for autocorrelation in time series data
- Getting ready
- How to do it…
- How it works…
- There's more...
- See also
- Chapter 10: Building Univariate Time Series Models Using Statistical Methods
- Technical requirements
- Plotting ACF and PACF
- How to do it…
- How it works…
- There's more...
- See also
- Forecasting univariate time series data with exponential smoothing
- How to do it…
- How it works…
- There's more…
- See also
- Forecasting univariate time series data with non-seasonal ARIMA
- Getting ready
- How to do it…
- How it works…
- There's more…
- See also
- Forecasting univariate time series data with seasonal ARIMA
- How to do it…
- How it works…
- There's more…
- See also
- Chapter 11: Additional Statistical Modeling Techniques for
Time Series
- Technical requirements
- Forecasting time series data using auto_arima
- Getting ready
- How to do it…
- How it works…
- There's more…
- See also
- Forecasting time series data using Facebook Prophet
- Getting ready
- How to do it…
- How it works…
- There's more…
- See also
- Forecasting multivariate time series data using VAR
- How to do it…
- How it works…
- There's more…
- See also...
- Evaluating vector autoregressive (VAR) models
- How to do it...
- How it works...
- There's more...
- See also…
- Forecasting volatility in financial time series data with GARCH
- Getting ready
- How to do it…
- How it works…
- There's more...
- See also
- Chapter 12: Forecasting Using Supervised Machine Learning
- Technical requirements
- Understanding supervised machine learning
- Preparing time series data for supervised learning
- How to do it…
- How it works...
- There's more...
- See also
- One-step forecasting using linear regression models with scikit-learn
- How to do it...
- How it works...
- There's more...
- See also
- Multi-step forecasting using linear regression models with scikit-learn
- How to do it...
- How it works...
- There's more…
- See also
- Forecasting using non-linear models with sktime
- How to do it...
- How it works...
- There's more...
- See also
- Optimizing a forecasting model with hyperparameter tuning
- Getting ready
- How to do it...
- How it works...
- There's more...
- See also
- Forecasting with exogenous variables and ensemble learning
- Getting ready
- How to do it...
- How it works...
- There's more...
- See also
- Chapter 13: Deep Learning for Time Series Forecasting
- Technical requirements
- Installing the deep learning libraries
- Understanding artificial neural networks
- Forecasting with an RNN using Keras
- How to do it...
- How it works...
- There's more...
- See also
- Forecasting with LSTM using Keras
- How to do it...
- How it works...
- There's more...
- See also
- Forecasting with a GRU using Keras
- How to do it...
- How it works...
- There's more...
- See also
- Forecasting with an RNN using PyTorch
- Getting ready
- How to do it...
- How it works...
- There's more...
- See also
- Forecasting with LSTM using PyTorch
- How to do it...
- How it works...
- There's more...
- See also
- Forecasting with a GRU using PyTorch
- How to do it...
- How it works...
- There's more...
- See also
- Technical requirements
- Chapter 14: Outlier Detection Using Unsupervised Machine Learning
- Technical requirements
- Detecting outliers using KNN
- How to do it...
- How it works...
- There's more...
- See also
- Detecting outliers using LOF
- How to do it...
- How it works...
- There's more...
- See also
- Detecting outliers using iForest
- How to do it...
- How it works...
- There's more...
- See also
- Detecting outliers using One-Class Support Vector Machine (OCSVM)
- How to do it...
- How it works...
- There's more...
- See also
- Detecting outliers using COPOD
- How to do it...
- How it works...
- There's more...
- See also
- Detecting outliers with PyCaret
- Getting ready
- How to do it...
- How it works...
- See also
- Chapter 15: Advanced Techniques for Complex Time Series
- Technical requirements
- Understanding state-space models
- Decomposing time series with multiple seasonal patterns using MSTL
- Getting ready
- How to do it...
- How it works...
- There's more...
- See also
- Forecasting with multiple seasonal
patterns using the Unobserved
Components Model (UCM)
- How to do it...
- How it works...
- There's more...
- See also
- Forecasting time series with multiple seasonal patterns using Prophet
- How to do it...
- How it works...
- There's more...
- See also
- Forecasting time series with multiple seasonal patterns using NeuralProphet
- Getting ready
- How to do it...
- How it works...
- There's more...
- See also
- Index
- Other Books You May Enjoy
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