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- Time Series Forecasting in Python
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Mahsulot #:
123736949
Time Series Forecasting in Python
87% of respondents would recommend this to a friend
Mahsulot #:
123736949
UZS 837541
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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.
Mahsulot tafsilotlari
- 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" has received high praise from customers for its clear and comprehensive approach to introducing the often complex subject of time series forecasting. Reviewers frequently mention that it serves as an excellent starting point for beginners, offering a structured journey from basic concepts to more advanced techniques. The explanations are notably precise, with an almost line-by-line breakdown of Python code, which helps readers grasp the material easily. This clarity is highlighted as a significant strength, allowing for a smoother learning curve without overwhelming the reader. Furthermore, the book incorporates the latest developments in applying machine learning to time series forecasting, alongside classical methods in Python. This combination provides a well-rounded introduction to both contemporary and foundational techniques. Many readers expressed satisfaction with the flow of the content, specifically noting that the author gradually increases complexity, making it accessible for those without a strong background in data science or programming. However, some feedback pointed out that the transition to machine learning concepts occurs rather quickly, which may pose a challenge for certain readers. Despite this minor critique, the overall sentiment leans positively, with many customers feeling encouraged to pursue further learning, particularly in TensorFlow, as a result of their exposure to the material in this book. **
Customer Reviews & Ratings
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Pros
- Clear and comprehensive explanations, making it beginner-friendly.
- Gradually increasing complexity that helps in understanding.
- Detailed breakdown of Python code for practical application.
- Covers both classical methods and contemporary machine learning approaches.
Kamchiliklari
- Some readers feel the transition to machine learning is too abrupt.
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Xususiyatlar va Afzalliklar
- 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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