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Forecasting Time Series Data with Facebook Prophet

Forecasting Time Series Data with Facebook Prophet

By : Greg Rafferty
4.9 (17)
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Forecasting Time Series Data with Facebook Prophet

Forecasting Time Series Data with Facebook Prophet

4.9 (17)
By: Greg Rafferty

Overview of this book

Prophet enables Python and R developers to build scalable time series forecasts. This book will help you to implement Prophet’s cutting-edge forecasting techniques to model future data with higher accuracy and with very few lines of code. You will begin by exploring the evolution of time series forecasting, from the basic early models to the advanced models of the present day. The book will demonstrate how to install and set up Prophet on your machine and build your first model with only a few lines of code. You'll then cover advanced features such as visualizing your forecasts, adding holidays, seasonality, and trend changepoints, handling outliers, and more, along with understanding why and how to modify each of the default parameters. Later chapters will show you how to optimize more complicated models with hyperparameter tuning and by adding additional regressors to the model. Finally, you'll learn how to run diagnostics to evaluate the performance of your models and see some useful features when running Prophet in production environments. By the end of this Prophet book, you will be able to take a raw time series dataset and build advanced and accurate forecast models with concise, understandable, and repeatable code.
Table of Contents (18 chapters)
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1
Section 1: Getting Started
4
Section 2: Seasonality, Tuning, and Advanced Features
13
Section 3: Diagnostics and Evaluation

Chapter 12: Performance Metrics

No model of a real-world phenomenon is perfect. There are countless statistical assumptions made about the underlying data, there is noise in the measurements, and there are unknown and unmodeled contributing factors to the output. But even though it is not perfect, a good model is still informative and valuable. So, how do you know whether you have such a good model? How can you be sure your predictions for the future can be trusted? Cross-validation got us part of the way there, by providing a technique to compare unbiased predictions to actual values. This chapter is all about how to compare different models.

Prophet features several different metrics that are used for comparing your actual values with your predicted values, so you can quantify the performance of your model. This tells you how good your model actually is and whether you can trust the predictions, and helps you compare the performance of different models so you can choose which...

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Forecasting Time Series Data with Facebook Prophet
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