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Time Series Analysis Python by Atwan Tarek (16 results)
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Time Series Analysis with Python Cookbook: Practical recipes for exploratory data analysis, data preparation, forecasting, and model evaluation
Published by Packt Publishing (edition ), 2022
ISBN 10: 1801075549 ISBN 13: 9781801075541
Language: English
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Time Series Analysis with Python Cookbook: Practical recipes for exploratory data analysis, data preparation, forecasting, and model evaluation
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Time Series Analysis with Python Cookbook: Practical recipes for exploratory data analysis, data preparation, forecasting, and model evaluation
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Time Series Analysis with Python Cookbook: Practical recipes for exploratory data analysis, data preparation, forecasting, and model evaluation
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Time Series Analysis with Python Cookbook: Practical recipes for exploratory data analysis, data preparation, forecasting, and model evaluation
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Time Series Analysis with Python Cookbook: Practical recipes for exploratory data analysis, data preparation, forecasting, and model evaluation (Paperback or Softback)
Published by Packt Publishing 6/30/2022, 2022
ISBN 10: 1801075549 ISBN 13: 9781801075541
Language: English
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Add to basketPaperback or Softback. Condition: New. Time Series Analysis with Python Cookbook: Practical recipes for exploratory data analysis, data preparation, forecasting, and model evaluation 2.35. Book.
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Time Series Analysis with Python Cookbook
Published by Packt Publishing Limited, 2022
ISBN 10: 1801075549 ISBN 13: 9781801075541
Language: English
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Time Series Analysis with Python Cookbook: Over 75 practical recipes to develop your time series analysis and forecasting skills
Published by Packt Publishing Limited, 2022
ISBN 10: 1801075549 ISBN 13: 9781801075541
Language: English
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Time Series Analysis with Python Cookbook
Published by Packt Publishing Limited, 2022
ISBN 10: 1801075549 ISBN 13: 9781801075541
Language: English
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Time Series Analysis with Python Cookbook
Published by Packt Publishing, Limited, 2022
ISBN 10: 1801075549 ISBN 13: 9781801075541
Language: English
Condition: New. Print on Demand pp. 624.
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Add to basketTaschenbuch. Condition: Neu. nach der Bestellung gedruckt Neuware - Printed after ordering - Perform time series analysis and forecasting confidently with this Python code bank and reference manualKey Features:Explore forecasting and anomaly detection techniques using statistical, machine learning, and deep learning algorithmsLearn different techniques for evaluating, diagnosing, and optimizing your modelsWork with a variety of complex data with trends, multiple seasonal patterns, and irregularitiesBook 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 dataApply various imputation and interpolation strategies for missing dataImplement different models for univariate and multivariate time seriesUse different deep learning libraries such as TensorFlow, Keras, and PyTorchPlot interactive time series visualizations using hvPlotExplore state-space models and the unobserved components model (UCM)Detect anomalies using statistical and machine learning methodsForecast complex time series with multiple seasonal patternsWho 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.
