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001			241810
003			IT-RoAPU
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008			221201t20212021gw fo d z eng d
020			_a9783110671100 _qprint
020			_a9783110671209 _qEPUB
020			_a9783110671124 _qPDF
024	7		_a10.1515/9783110671124 _2doi
035			_a(DE-B1597)9783110671124
035			_a(DE-B1597)534678
035			_a(OCoLC)1243310393
040			_aDE-B1597 _beng _cDE-B1597 _erda
050		4	_aQA76.9.D343 _bV36 2021
072		7	_aBUS065000 _2bisacsh
084			_aonline - DeGruyter
100	1		_aVandeput, Nicolas _eautore
245	1	0	_aData Science for Supply Chain Forecasting / _cNicolas Vandeput.
250			_a2nd ed.
264		1	_aBerlin ; _aBoston : _bDe Gruyter, _c[2021]
264		4	_c©2021
300			_a1 online resource (XXVIII, 282 p.)
336			_atext _btxt _2rdacontent
337			_acomputer _bc _2rdamedia
338			_aonline resource _bcr _2rdacarrier
347			_atext file _bPDF _2rda
505	0	0	_tFrontmatter -- _tAcknowledgments -- _tAbout the Author -- _tForeword – Second Edition -- _tForeword – First Edition -- _tContents -- _tIntroduction -- _tPart I: Statistical Forecasting -- _t1 Moving Average -- _t2 Forecast KPI -- _t3 Exponential Smoothing -- _t4 Underfitting -- _t5 Double Exponential Smoothing -- _t6 Model Optimization -- _t7 Double Smoothing with Damped Trend -- _t8 Overfitting -- _t9 Triple Exponential Smoothing -- _t10 Outliers -- _t11 Triple Additive Exponential Smoothing -- _tPart II: Machine Learning -- _t12 Machine Learning -- _t13 Tree -- _t14 Parameter Optimization -- _t15 Forest -- _t16 Feature Importance -- _t17 Extremely Randomized Trees -- _t18 Feature Optimization #1 -- _t19 Adaptive Boosting -- _t20 Demand Drivers and Leading Indicators -- _t21 Extreme Gradient Boosting -- _t22 Categorical Features -- _t23 Clustering -- _t24 Feature Optimization #2 -- _t25 Neural Networks -- _tPart III: Data-Driven Forecasting Process Management -- _t26 Judgmental Forecasts -- _t27 Forecast Value Added -- _tNow It’s Your Turn! -- _tA Python -- _tBibliography -- _tGlossary -- _tIndex
506	0		_arestricted access _uhttp://purl.org/coar/access_right/c_16ec _fonline access with authorization _2star
520			_aUsing data science in order to solve a problem requires a scientific mindset more than coding skills. Data Science for Supply Chain Forecasting, Second Edition contends that a true scientific method which includes experimentation, observation, and constant questioning must be applied to supply chains to achieve excellence in demand forecasting. This second edition adds more than 45 percent extra content with four new chapters including an introduction to neural networks and the forecast value added framework. Part I focuses on statistical "traditional" models, Part II, on machine learning, and the all-new Part III discusses demand forecasting process management. The various chapters focus on both forecast models and new concepts such as metrics, underfitting, overfitting, outliers, feature optimization, and external demand drivers. The book is replete with do-it-yourself sections with implementations provided in Python (and Excel for the statistical models) to show the readers how to apply these models themselves. This hands-on book, covering the entire range of forecasting—from the basics all the way to leading-edge models—will benefit supply chain practitioners, forecasters, and analysts looking to go the extra mile with demand forecasting.
530			_aIssued also in print.
538			_aMode of access: Internet via World Wide Web.
546			_aIn English.
588	0		_aDescription based on online resource; title from PDF title page (publisher's Web site, viewed 01. Dez 2022)
650		0	_aBusiness forecasting _xData processing.
650		0	_aData mining _xStatistical methods.
650		0	_aPython (Computer program language).
650		7	_aBUSINESS & ECONOMICS / Total Quality Management. _2bisacsh
653			_aData science.
653			_aDe Gruyter.
653			_aForecasting.
653			_aMachine learning.
653			_aNicolas Vandeput.
653			_aOverfit.
653			_aPython.
653			_aSKU Science.
653			_aSupChains.
653			_aSupply chain forecasting.
653			_aSupply chain.
653			_aUnderfit.
653			_ademand forecasting.
653			_ainventory optimisation.
653			_ainventory optimization.
653			_amulti-echelon optimisation.
653			_amulti-echelon optimization.
653			_asupply chain data science.
653			_asupply chain management.
700	1		_aMakridakis, Spyros _eautore
700	1		_aNdiaye, Alassane B. _eautore
850			_aIT-RoAPU
856	4	0	_uhttps://doi.org/10.1515/9783110671124
856	4	0	_uhttps://www.degruyter.com/isbn/9783110671124
856	4	2	_3Cover _uhttps://www.degruyter.com/document/cover/isbn/9783110671124/original
942			_cEB
999			_c241810 _d241810