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Item #:
19823309
Forecasting: principles and practice
85% of respondents would recommend this to a friend
Item #:
19823309
VUV 14287
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What Stands Out
Comprehensive Coverage
Offers an extensive exploration of forecasting methods and techniques, catering to both academic and practical applications, making it ideal for students and professionals in data analysis.
Updated Content
Includes the latest research and advancements in forecasting, ensuring readers are equipped with contemporary practices and insights that enhance their analytical skills.
Practical Examples
Provides real-world case studies and practical examples to illustrate concepts, facilitating better understanding and application of forecasting methods in various industries.
Product Details
| Publisher | OTexts |
| Publication date | May 6, 2018 |
| Edition | 2nd |
| Language | English |
| Print length | 382 pages |
| ISBN-10 | 0987507117 |
| ISBN-13 | 978-0987507112 |
| Item Weight | 1.76 pounds (800 grams) |
| Dimensions | 6.69 x 0.9 x 9.61 inches (17 x 2.3 x 24.4 cm) |
Who Should Buy?
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Students and Scholars
Ideal for those studying forecasting techniques and statistical methods in academia or research environments.
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Business Analysts
Useful for individuals involved in data analysis and forecasting for business strategy and decision-making.
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Data Scientists
Provides essential insights and methodologies for data scientists working with predictive models and analytics.
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Casual Readers
Not suitable for those looking for light reading or general information without technical depth.
Product Description
Forecasting: principles and practice
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Planning & Forecasting Editorial Review
Forecasting: principles and practice is an exceptional resource for anyone interested in mastering forecasting techniques using R. The book strikes a balance between theoretical concepts and practical application, presenting major forecasting methods such as regressions, exponential smoothing, and ARIMA in an accessible manner. Many readers appreciate the hands-on approach, with integrated R code and end-of-chapter exercises designed for both novice and experienced data scientists. While some topics like outlier detection may not be covered in depth, the authors provide ample references for further exploration. Overall, this book serves as a comprehensive, engaging textbook for forecasting enthusiasts and students alike.
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Pros
- Excellent integration of R code throughout the text
- Covers major forecasting methods in detail
- Hands-on approach with practical examples
- Suitable for both beginners and advanced users
- Provides exercises for better understanding
Cons
- Some advanced topics are not covered in detail
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