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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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Forecasting with exogenous variables and ensemble learning

This recipe will allow you to explore two different techniques: working with multivariate time series and using ensemble forecasters. For example, the EnsembleForecaster class takes in a list of multiple regressors, each regressor gets trained, and collectively contribute in making a prediction. This is accomplished by taking the average of the individual predictions from each regressor. Think of this as the power of the collective. You will use the same regressors you used earlier: Linear Regression, Random Forest Regressor, Gradient Boosting Regressor, and Support Vector Machines Regressor.

You will use a Naive Regressor as the baseline to compare with EnsembleForecaster. Additionally, you will use exogenous variables with the Ensemble Forecaster to model a multivariate time series. You can use any regressor that accepts exogenous variables.

Getting ready

You will load the same modules and libraries from the previous...

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