Time Series Forecasting in Python
Time Series Forecasting in Python
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Time Series Forecasting in Python
Time Series Forecasting in Python
Build predictive models from time-based patterns in your data.
Time Series Forecasting in Python
Nº de artículo: 89402933

Time Series Forecasting in Python

88% of respondents would recommend this to a friend

Nº de artículo: 89402933

DOP 5161

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What Stands Out

Comprehensive Toolkit
Offers a wide array of libraries and tools for effective time series analysis, including statistical models, machine learning techniques, and data visualization to enhance prediction accuracy and insights.
User-Friendly Libraries
Utilizes popular libraries like Pandas and StatsModels for streamlined data manipulation and forecasting, making it accessible for beginners while remaining powerful for expert analysts.
Community Support
Backed by a large community and extensive online resources, allowing users to find solutions, tutorials, and forums that enhance learning and troubleshooting during the forecasting process.

Detalles de producto

Shop Time Series Forecasting in Python online at a best price in Dominican Republic. 161729988X
  • 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 processTime 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 technologyYou can predict the future—with a little help from Python, deep learning, and time series data! Time series forecasting is a technique for modeling time-centric data to identify upcoming events. New Python libraries and powerful deep learning tools make accurate time series forecasts easier than ever before.About the bookTime Series Forecasting in Python teaches you how to get immediate, meaningful predictions from time-based data such as logs, customer analytics, and other event streams. In this accessible book, you’ll learn statistical and deep learning methods for time series forecasting, fully demonstrated with annotated Python code. Develop your skills with projects like predicting the future volume of drug prescriptions, and you’ll soon be ready to build your own accurate, insightful forecasts.What's insideCreate models for seasonal effects and external variablesMultivariate forecasting models to predict multiple time seriesDeep learning for large datasetsAutomate the forecasting processAbout the readerFor data scientists familiar with Python and TensorFlow.About the authorMarco Peixeiro is a seasoned data science instructor who has worked as a data scientist for one of Canada’s largest banks.Table of ContentsPART 1 TIME WAITS FOR NO ONE1 Understanding time series forecasting2 A naive prediction of the future3 Going on a random walkPART 2 FORECASTING WITH STATISTICAL MODELS4 Modeling a moving average process5 Modeling an autoregressive process6 Modeling complex time series7 Forecasting non-stationary time series8 Accounting for seasonality9 Adding external variables to our model10 Forecasting multiple time series11 Capstone: Forecasting the number of antidiabetic drug prescriptions in AustraliaPART 3 LARGE-SCALE FORECASTING WITH DEEP LEARNING12 Introducing deep learning for time series forecasting13 Data windowing and creating baselines for deep learning14 Baby steps with deep learning15 Remembering the past with LSTM16 Filtering a time series with CNN17 Using predictions to make more predictions18 Capstone: Forecasting the electric power consumption of a householdPART 4 AUTOMATING FORECASTING AT SCALE19 Automating time series forecasting with Prophet20 Capstone: Forecasting the monthly average retail price of steak in Canada21 Going above and beyond
Publisher Manning Publications
Publication date October 4, 2022
Language English
Print length 456 pages
ISBN-10 161729988X
ISBN-13 978-1617299889
Item Weight 1.55 pounds (700 grams)
Dimensions 7.38 x 1.14 x 9.25 inches (18.7 x 2.9 x 23.5 cm)

Who Should Buy?

Suitable For
  • Data Scientists

    Ideal for data scientists wanting to enhance their skills in time series analysis and prediction techniques.

  • Business Analysts

    Beneficial for business analysts who need to predict trends and make data-driven decisions.

  • Students and Learners

    Perfect for students learning data analysis and forecasting concepts using Python through practical applications.

Not Suitable For
  • Complete Beginners

    Not suitable for those new to programming or Python, as foundational knowledge is assumed.

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Probability & Statistics Editorial Review

In reviewing "Time Series Forecasting in Python," it's clear that customer experiences vary widely, reflecting both appreciation and disappointment with the book's content and structure. Many readers highlight its strengths, particularly its accessibility for beginners and clear explanations of complex concepts. The book skillfully guides readers from foundational principles to more advanced topics, making it particularly valuable for non-technical individuals. The integration of Python code with theoretical concepts is praised as it enhances understanding, allowing readers to grasp how to apply machine learning to time series forecasting effectively. However, some customers express significant issues with the book. A recurring critique is its failure to demonstrate how to forecast beyond the dataset at hand. Readers expect books on forecasting to teach future prediction methods, but many feel this book falls short in that regard. Additionally, criticisms have been raised about the apparent redundancy in the text, leading to perceptions of unnecessary length and a high price tag that some believe is not justified by the content provided. Overall, while the book offers a well-structured introduction to time series forecasting with Python that is beneficial for beginners, it may not meet the expectations of those looking to advance into future forecasting techniques. **

Customer Reviews & Ratings

4.1
35 valoraciones de los clientes
  • 5 estrella
    63%
  • 4 estrella
    15%
  • 3 estrella
    5%
  • 2 estrella
    6%
  • 1 estrella
    11%

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ventajas

  • Clear explanations, especially for Python code, beneficial for beginners.
  • Covers a range of topics from basic to more sophisticated concepts effectively.
  • Good for non-technical readers due to its non-mathematical approach.
  • Unique structure that integrates concepts with practical Python examples.

Contras

  • Fails to demonstrate forecasting beyond available datasets.

Product Price History

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características y beneficios

  • 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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