000			03531nmm a22003497a 4500
003			OSt
005			20240812145733.0
008			240812b |||||||| |||| 00| 0 eng d
020			_a9781837635504 (e-book)
040			_cCavite State University - Main Campus Library _erda
041			_aeng
050			_aQA280 _bR12 2023
100			_aRafferty, Greg _928860 _eauthor
245			_aForecasting time series data with Prophet : _bbuild, improve, and optimize time series forecasting models using Meta's advanced forecasting tool / _c by Greg Rafferty
250			_a2nd. ed.
260			_aBirmingham, UK : _bPackt Publishing Ltd, _c2023
300			_a1 online resource (282, pages) : _bcolor illustrations.
336			_2rdacontent _atext
337			_2rdamedia _acomputer
338			_2rdacarrier _aonline resource
500			_ahttps://portal.igpublish.com/iglibrary/ is required to read this e-book.
504			_aIncludes index
505			_aPart 1. Getting started with prophet -- 1. The history and development of time series forecasting -- 2. Getting started with prophet -- 3. How prophet works -- Part 2. Seasonality, tuning, and advanced features -- 4. Handling non-daily data -- 5. Working with seasonality -- 6. Forecasting holiday effects -- 7. Controlling growth modes -- 8. Influencing trend changepoints -- 9. Including additional regressors -- 10. Accounting for outliers and special events -- 11. Managing uncertainty intervals -- Part 3. Diagnostics and evaluation -- 12. Performing cross-validation -- 13. Evaluating performance metrics -- 14. Productional zing prophet
520			_aProphet 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 fi rst 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.
541			_aFund 164 _bCE-Logic _cPurchased _dFebruary 19, 2024 _eOEBP000454 _fP. Roderno _hPHP 5,586.00 _p2024-02-0124 _q2024-1-113
650		0	_aTime-series analysis _92379 _xData processing
650		0	_aPython (Computer program language) _94472
650		0	_aMachine learning _917626
856			_uhttps://portal.igpublish.com/iglibrary/obj/PACKT0006618?searchid=1720070661322Q2BFX9HcjrPbzU47fZ4B3 _yClick here to read Full-Text E-Book
856			_uhttps://docs.google.com/forms/d/e/1FAIpQLSfSoAj3qM4b_ttQMZLuimqgwkfHDH1NyJ7S4eyjHD7Vr4j7EQ/viewform _yLog-in to the website is required to read this e-book. Click here to request access.
942			_2lcc _cOEB
999			_c64482 _d64482