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Item #:
15651420
Practical Time Series Analysis: Prediction with Statistics and Machine Learning
90% of respondents would recommend this to a friend
Item #:
15651420
AUD 98
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What Stands Out
Comprehensive Coverage
Offers detailed insights into both statistical methods and machine learning techniques for time series analysis, enabling users to tackle varied data forecasting scenarios effectively.
User-Friendly Approach
Designed for both beginners and experienced analysts, the book simplifies complex concepts, ensuring easy comprehension and practical application for real-world predictions.
Real-World Examples
Includes practical case studies and examples that illustrate the application of techniques, allowing readers to connect theory with practice for effective learning.
Product Details
| Publisher | O'Reilly Media |
| Publication date | November 19, 2019 |
| Edition | 1st |
| Language | English |
| Print length | 497 pages |
| ISBN-10 | 1492041653 |
| ISBN-13 | 978-1492041658 |
| Item Weight | 2.31 pounds (1.05 kg) |
| Dimensions | 7 x 1 x 9.25 inches (17.8 x 2.5 x 23.5 cm) |
Who Should Buy?
-
Data Scientists
Ideal for data scientists looking to enhance their predictive modeling skills using time series data.
-
Graduate Students
Perfect for graduate students pursuing courses in statistics, machine learning, or data analysis for comprehensive learning.
-
Business Analysts
Useful for business analysts needing to forecast trends for informed decision-making and strategic planning.
-
Beginner Statisticians
Not suitable for those with no prior knowledge of statistics or machine learning concepts.
Product Description
Practical Time Series Analysis: Prediction with Statistics and Machine Learning
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Intelligence & Semantics Editorial Review
The 1st Edition of the book "Practical Time Series Analysis: Prediction with Statistics and Machine Learning" has received mixed reviews from customers. Some readers appreciated the practical approach and found it to be an easy read, especially for individuals with a machine learning background. However, others expressed disappointment, highlighting issues such as disorganized content, poorly written example codes, the author's inConsistent use of programming languages (Python and R), and concerns about plagiarism and lack of documentation in the codes. The book garnered criticism for its verbose and disorganized nature, with readers noting that it felt as though the author was speaking to herself without Considering readability. They pointed out that the text includes copied definitions from Wikipedia and an overreliance on blog posts rather than citing scientific literature. Additionally, some readers found the examples to be unhelpful and not easily reproducible, expressing regret over their purchase and feeling that the book was a waste of time and money.
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Pros
- Provides a practical approach to time series analysis
- Includes useful tips and tricks
- Offers some catchy concepts, such as AR and MA models, that readers found memorable
Cons
- InConsistent use of Python and R may make it difficult for readers working exclusively in one language
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