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Introduction to Time Series Forecasting With Python: How to Prepare Data and Develop Models to Predict the Future
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Название: Introduction to Time Series Forecasting With Python: How to Prepare Data and Develop Models to Predict the Future
Автор: Jason Brownlee
Издательство: Machine Learning Mastery Pty. Ltd.
Год: 2018
Страниц: 366
Язык: английский
Формат: pdf (true)
Размер: 10.1 MB

Welcome to the Introduction to Time Series Forecasting with Python. You are one of those rare people that have decided to invest in your education and in your future and I am honored that I can help.

This book will show you how to make predictions on univariate time series problems using the standard tools in the Python ecosystem. Time series is an important and under served topic in applied Machine Learning (ML), Python is the growing platform for Machine Learning and predictive modeling, and this book unlocks time series for Python. But, you have to do the work. I will lay out all of the topics, the tools, the code and the templates, but it is up to you to put them into practice on your projects and get results.

Time series forecasting is different from other Machine Learning problems. The key difference is the fixed sequence of observations and the constraints and additional structure this provides.

In this mega Ebook written in the friendly Machine Learning Mastery style that you’re used to, finally cut through the math and specialized methods for time series forecasting.

Using clear explanations, standard Python libraries and step-by-step tutorials you will discover how to load and prepare data, evaluate model skill, and implement forecasting models for time series data.

SciPy is an ecosystem of Python libraries for mathematics, science, and engineering. It is an add-on to Python that you will need for time series forecasting. Two SciPy libraries provide a foundation for most others; they are NumPy 3 for providing efficient array operations and Matplotlib 4 for plotting data. There are three higher-level SciPy libraries that provide the key features for time series forecasting in Python. They are Pandas, Statsmodels, and scikit-learn for data handling, time series modeling, and machine learning respectively.

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