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Item #:
123736949
Time Series Forecasting in Python
87% of respondents would recommend this to a friend
Item #:
123736949
SAR 270
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What Stands Out
Comprehensive Guide
This book offers an in-depth exploration of time series forecasting methods, making it suitable for both beginners and experienced practitioners seeking to enhance their Python skills.
Practical Applications
Includes real-world examples and use cases, allowing readers to directly apply theoretical knowledge to practical scenarios in various industries.
Latest Techniques
Covers the most up-to-date forecasting techniques and tools in Python, ensuring readers are equipped with current knowledge and practices in data science.
Product Details
- Build predictive models from time-based patterns in your data. Master statistical models including new deep learning approaches for time series forecasting. In Time Series Forecasting in Python you will learn how to: Recognize a time series forecasting problem and build a performant predictive modelCreate univariate forecasting models that account for seasonal effects and external variablesBuild multivariate forecasting models to predict many time series at onceLeverage large datasets by using deep learning for forecasting time seriesAutomate the forecasting process DESCRIPTION Time Series Forecasting in Python teaches you to build powerful predictive models from time-based data. Every model you create is relevant, useful, and easy to implement with Python. You'll explore interesting real-world datasets like Google's daily stock price and economic data for the USA, quickly progressing from the basics to developing large-scale models that use deep learning tools like TensorFlow.Time Series Forecasting in Python teaches you to apply time series forecasting and get immediate, meaningful predictions. You'll learn both traditional statistical and new deep learning models for time series forecasting, all fully illustrated with Python source code. Time Series Forecasting in Python teaches you to build powerful predictive models from time-based data. Every model you create is relevant, useful, and easy to implement with Python. You'll explore interesting real-world datasets like Google's daily stock price and economic data for the USA, quickly progressing from the basics to developing large-scale models that use deep learning tools like TensorFlow. about the technologyTime series forecasting reveals hidden trends and makes predictions about the future from your data. This powerful technique has proven incredibly valuable across multiple fields―from tracking business metrics, to healthcare and the sciences. Modern Python libraries and powerful deep learning tools have opened up new methods and utilities for making practical time series forecasts. about the book Time Series Forecasting in Python teaches you to apply time series forecasting and get immediate, meaningful predictions. You'll learn both traditional statistical and new deep learning models for time series forecasting, all fully illustrated with Python source code. Test your skills with hands-on projects for forecasting air travel, volume of drug prescriptions, and the earnings of Johnson & Johnson. By the time you're done, you'll be ready to build accurate and insightful forecasting models with tools from the Python ecosystem.
| Publisher | Manning Publications |
| Publication date | 10 Nov. 2022 |
| Edition | 1st |
| Language | English |
| Print length | 456 pages |
| ISBN-10 | 161729988X |
| ISBN-13 | 978-1617299889 |
| Item weight | 703 g |
| Dimensions | 18.75 x 2.9 x 23.5 cm |
Who Should Buy?
-
Data Analysts
Ideal for data analysts seeking to enhance their forecasting skills using Python libraries and methods.
-
Students
Students studying statistics or data science, requiring a structured introduction to time series analysis in Python.
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Business Professionals
Business professionals who want to leverage predictive analytics to improve decision-making and strategic planning.
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Beginner Programmers
Not suitable for beginners unfamiliar with Python programming and statistical concepts, as prerequisites may overwhelm.
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Data Mining Editorial Review
Time Series Forecasting In Python offers a comprehensive guide for analysts and developers looking to deepen their understanding of time series data analysis. Published by Manning Publications on 10 Nov. 2022, this 456-page book is written in English and serves as the first edition. Readers can expect to gain practical insights into forecasting methods while utilizing Python tools effectively. The blend of theory and hands-on examples is particularly beneficial for those wanting an applied approach to time series forecasting, making it a valuable resource in data science and machine learning domains.
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Pros
- Clear explanations of complex concepts
- Practical examples with Python
- Well-structured for easy understanding
- Suitable for both beginners and experts
- In-depth coverage of forecasting techniques
Cons
- Some sections may require prior statistical knowledge
Product Price History
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Features & Benefits
- Create models that capture seasonal effects and external variables.
- Utilize multivariate forecasting to predict multiple time series effectively.
- Employ deep learning techniques for large datasets using Python.
- Automate your forecasting process for efficiency and accuracy.
- Access immediately applicable concepts that deliver real results.
- Written for data scientists looking to elevate their skills from R to Python.
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