Modern Time Series Forecasting with Python: Explore industry-ready time series forecasting using modern machine learning and deep learning
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Modern Time Series Forecasting with Python: Explore industry-ready time series forecasting using modern machine learning and deep learning
Build world-class time series forecasting systems and tackle real-world problems
Modern Time Series Forecasting with Python: Explore industry-ready time series forecasting using modern machine learning and deep learning
Numéro d'article: 62629275

Modern Time Series Forecasting with Python: Explore industry-ready time series forecasting using modern machine learning and deep learning

89% des répondants recommanderaient ceci à un ami

Numéro d'article: 62629275

BIF 219082

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Ce qui se démarque

Industry-Relevant Skills
Learn practical time series forecasting techniques that are in high demand across industries, ensuring you acquire skills that enhance your employability and marketability in the data science field.
Comprehensive Coverage
Explore both traditional and advanced deep learning methods to provide a thorough understanding of time series forecasting, equipping learners with versatile strategies to tackle diverse forecasting challenges.
Hands-On Projects
Engage in real-world projects that allow you to apply techniques to actual datasets, fostering experiential learning and reinforcing concepts through practical application in an interactive format.

Détails du produit

Shop Modern Time Series Forecasting with Python: Explore industry-ready time series forecasting using modern machine learning and deep learning online at a best price in Burundi. 1803246804
  • Explore industry-tested machine learning techniques used to forecast millions of time series
  • Get started with the revolutionary paradigm of global forecasting models
  • Apply new concepts to real-world datasets of energy forecasting
  • Discover how to manipulate and visualize time series data
  • Engineer features for machine learning models for forecasting
  • Learn about ensembling, stacking models, and global forecasting paradigm
Publisher Packt Publishing
Publication date November 24, 2022
Language English
Print length 552 pages
ISBN-10 1803246804
ISBN-13 978-1803246802
Item Weight 2.07 pounds (940 grams)
Dimensions 7.5 x 1.25 x 9.25 inches (19.1 x 3.2 x 23.5 cm)

À qui est-ce destiné ?

Suitable For
  • Data Scientists

    Ideal for data scientists looking to enhance their forecasting capabilities using advanced machine learning techniques and deep learning.

  • Business Analysts

    Perfect for business analysts aiming to leverage time series forecasting to inform strategic decisions in their organizations.

  • Students & Learners

    Beneficial for students or learners interested in gaining practical skills in time series analysis and forecasting methodologies.

Not Suitable For
  • Absolute Beginners

    Not suitable for those without foundational knowledge in Python or machine learning concepts, as foundational skills are assumed.

DESCRIPTION DU PRODUIT

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Questions et réponses des clients

  • question: What is time series forecasting?

    répondre: Time series forecasting is the process of predicting future values based on previously observed values. This approach is essential in various fields like finance for stock market predictions, supply chain management for inventory forecasting, and weather forecasting. By analyzing patterns and trends in historical data, practitioners can develop models that provide insights into future behavior, aiding in strategic decision-making.
  • question: How does Python facilitate time series forecasting?

    répondre: Python offers powerful libraries such as Pandas, NumPy, and Statsmodels, which simplify data manipulation and statistical modeling for time series analysis. Additionally, machine learning libraries like scikit-learn and deep learning frameworks such as TensorFlow and Keras enable the development of sophisticated forecasting models. Users can implement various techniques from classical to modern algorithms effectively, making Python a preferred choice among data scientists.
  • question: What techniques are covered in this book for forecasting?

    répondre: The book covers a range of techniques, including traditional statistical methods like ARIMA, as well as modern machine learning approaches, such as LSTM and Prophet. These methodologies provide a comprehensive toolkit for users, enabling them to tackle various forecasting challenges. The inclusion of practical examples showcases how to apply these techniques in real-world scenarios, helping users gain hands-on experience.
  • question: Who is this book suitable for?

    répondre: This book is suitable for data analysts, data scientists, and anyone interested in predictive analytics. It's designed for readers with a basic understanding of Python and statistical concepts. Whether you're a novice seeking to learn time series forecasting from scratch or an experienced practitioner looking to sharpen your skills with modern techniques, this resource provides valuable insights and practical applications.
  • question: Can beginners benefit from modern machine learning methods discussed in the book?

    répondre: Absolutely! The book provides clear explanations and step-by-step guides that will help beginners understand complex concepts. Each technique is introduced in a user-friendly manner, illustrated with practical examples. Using these modern machine learning methods, readers can witness the impact of data-driven decision-making in various scenarios, enhancing their learning experience effectively.
  • question: What real-world applications can I explore with the knowledge from this book?

    répondre: With the knowledge acquired from the book, readers can apply time series forecasting in various domains, including finance for stock price predictions, retail for sales forecasting, and healthcare for patient flow predictions. By implementing these techniques, users will gain valuable insights that can drive operational efficiencies and strategic initiatives, ultimately impacting business growth positively.
  • question: Is prior knowledge of machine learning required to understand this book?

    répondre: While prior knowledge of machine learning is helpful, it's not strictly necessary to understand the book. The author provides foundational concepts alongside advanced discussions, making the material accessible for readers at different levels. By gradually introducing the basics of machine learning in the context of time series, the book ensures that all readers can follow along and progress their skills.
  • question: What kind of models can I expect to build after reading this book?

    répondre: After reading the book, you'll be equipped to build a variety of models suitable for time series forecasting. These include traditional models like ARIMA and exponential smoothing, as well as advanced machine learning models like Random Forests, Gradient Boosting, and deep learning models such as LSTM. Readers will be prepared to choose appropriate models based on their data characteristics and forecasting needs.
  • question: Is additional software required to practice the techniques from the book?

    répondre: To practice the techniques highlighted in the book, you'll need to have Python and several libraries installed, including Pandas, NumPy, and TensorFlow. The book provides guidance on setting up your environment, ensuring you can easily run the provided examples. Familiarizing yourself with these tools will enhance your capability to experiment with different forecasting methodologies.
  • question: Where can I buy Modern Time Series Forecasting with Python?

    répondre: You can buy Modern Time Series Forecasting with Python online through Ubuy in Burundi. Ubuy is known for offering a wide selection of books, ensuring you can easily find this title along with other educational resources. Simply visit Ubuy's website, search for the book, and secure your copy to start enhancing your forecasting skills.

Probability & Statistics Editorial Review

Modern Time Series Forecasting with Python: Explore industry-ready time series forecasting using modern machine learning and deep learning is a comprehensive guide that spans over 500 pages, offering detailed explanations of various forecasting techniques, including ARIMA and deep learning models. Readers commend the author for covering critical topics and providing valuable code examples, accessible through GitHub for free. The book is particularly lauded for its structured layout and thorough approach, making it accessible for both beginners and experienced practitioners looking to enhance their skills. Testimonials highlight its practical use in real-world projects, emphasizing its utility in tackling complex forecasting problems with AI techniques.

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Avantages

  • Comprehensive coverage of time series forecasting techniques
  • Practical coding examples available on GitHub
  • Great for beginners and experienced data scientists
  • Detailed exploration of data wrangling concepts
  • Includes insights on modern machine learning methods

Les inconvénients

  • Some may find the smaller font challenging to read

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Caractéristiques et avantages

  • Explore industry-tested machine learning and deep learning techniques
  • Learn to analyze, visualize, and create state-of-the-art forecasting systems
  • Develop ML and DL models for time series forecasting
  • Discover ensembling and stacking models for improved accuracy
  • Understand and apply cutting-edge DL models such as N-BEATS and Autoformer
  • Explore multi-step forecasting and cross-validation strategies

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