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Forecasting
| Forecasting |
| Pubbl/distr/stampa | Basel, Switzerland : , : MDPI AG, , 2019- |
| Descrizione fisica | 1 online resource |
| Disciplina | 330 |
| Soggetto topico |
Forecasting
Forecasting - Mathematical models Prévision |
| Soggetto genere / forma |
Periodicals.
Zeitschrift |
| ISSN | 2571-9394 |
| Formato | Materiale a stampa  |
| Livello bibliografico | Periodico |
| Lingua di pubblicazione | eng |
| Record Nr. | UNINA-9910309942103321 |
| Basel, Switzerland : , : MDPI AG, , 2019- | ||
| Materiale a stampa | ||
| Lo trovi qui: Univ. Federico II | ||
| ||
Forecasting
| Forecasting |
| Pubbl/distr/stampa | Basel, Switzerland : , : MDPI AG, , 2019- |
| Descrizione fisica | 1 online resource |
| Disciplina | 330 |
| Soggetto topico |
Forecasting
Forecasting - Mathematical models Prévision |
| Soggetto genere / forma |
Periodicals.
Zeitschrift |
| ISSN | 2571-9394 |
| Formato | Materiale a stampa |
| Livello bibliografico | Periodico |
| Lingua di pubblicazione | eng |
| Record Nr. | UNISA-996321216803316 |
| Basel, Switzerland : , : MDPI AG, , 2019- | ||
| Materiale a stampa | ||
| Lo trovi qui: Univ. di Salerno | ||
| ||
Forecasting principles and applications / Stephen A. DeLurgio
| Forecasting principles and applications / Stephen A. DeLurgio |
| Autore | DeLurgio, Stephen A |
| Pubbl/distr/stampa | Boston : Irwin/McGraw-Hill, c1998 |
| Descrizione fisica | xxviii, 802 p. : ill. ; 26 cm. + 1 computer disk (3 1/2 in.) |
| Disciplina |
003.2015195
658.15224 |
| Soggetto topico |
Forecasting - Mathematical models
Forecasting - Statistical methods |
| ISBN | 0071159983 |
| Formato | Materiale a stampa |
| Livello bibliografico | Monografia |
| Lingua di pubblicazione | eng |
| Record Nr. | UNISALENTO-991004063519707536 |
DeLurgio, Stephen A
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| Boston : Irwin/McGraw-Hill, c1998 | ||
| Materiale a stampa | ||
| Lo trovi qui: Univ. del Salento | ||
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Journal of forecasting
| Journal of forecasting |
| Pubbl/distr/stampa | [Chichester] ; ; [New York, N.Y.], : John Wiley & Sons |
| Descrizione fisica | 1 online resource |
| Disciplina | 003 |
| Soggetto topico |
Forecasting
Forecasting - Mathematical models Prévision Prévision - Modèles mathématiques Forecasting & Prediction Prognose Zeitschrift Online-Ressource |
| Soggetto genere / forma | Periodicals. |
| Soggetto non controllato | Forecasting - Serials |
| ISSN | 1099-131X |
| Formato | Materiale a stampa |
| Livello bibliografico | Periodico |
| Lingua di pubblicazione | eng |
| Altri titoli varianti | Forecasting |
| Record Nr. | UNISA-996210868303316 |
| [Chichester] ; ; [New York, N.Y.], : John Wiley & Sons | ||
| Materiale a stampa | ||
| Lo trovi qui: Univ. di Salerno | ||
| ||
Journal of forecasting
| Journal of forecasting |
| Pubbl/distr/stampa | [Chichester] ; ; [New York, N.Y.], : John Wiley & Sons |
| Descrizione fisica | 1 online resource |
| Disciplina | 003 |
| Soggetto topico |
Forecasting
Forecasting - Mathematical models Prévision Prévision - Modèles mathématiques Forecasting & Prediction Prognose Zeitschrift Online-Ressource |
| Soggetto genere / forma | Periodicals. |
| Soggetto non controllato | Forecasting - Serials |
| ISSN | 1099-131X |
| Formato | Materiale a stampa |
| Livello bibliografico | Periodico |
| Lingua di pubblicazione | eng |
| Altri titoli varianti | Forecasting |
| Record Nr. | UNINA-9910142992503321 |
| [Chichester] ; ; [New York, N.Y.], : John Wiley & Sons | ||
| Materiale a stampa | ||
| Lo trovi qui: Univ. Federico II | ||
| ||
Learning predictive analytics with Python : gain practical insights into predictive modelling by implementing predictive analytics algorithms on public datasets with Python / / Ashish Kumar ; [foreword by Pradeep Gulipalli, co-founder and head of India operations - Tiger Analytics]
| Learning predictive analytics with Python : gain practical insights into predictive modelling by implementing predictive analytics algorithms on public datasets with Python / / Ashish Kumar ; [foreword by Pradeep Gulipalli, co-founder and head of India operations - Tiger Analytics] |
| Autore | Kumar Ashish (Analyst) |
| Edizione | [First edition] |
| Pubbl/distr/stampa | Birmingham : , : Packt Publishing, , 2016 |
| Descrizione fisica | 1 online resource (354 pages) |
| Collana | Community experience distilled |
| Soggetto topico |
Python (Computer program language)
R (Computer program language) Decision making - Statistical methods Forecasting - Mathematical models |
| ISBN | 1-78398-327-2 |
| Formato | Materiale a stampa |
| Livello bibliografico | Monografia |
| Lingua di pubblicazione | eng |
| Nota di contenuto |
Cover ; Copyright; Credits; Foreword; About the Author; Acknowledgments; About the Reviewer; www.PacktPub.com; Table of Contents; Preface; Chapter 1: Getting Started with Predictive Modelling ; Introducing predictive modelling; Scope of predictive modelling; Ensemble of statistical algorithms; Statistical tools; Historical data; Mathematical function; Business context; Knowledge matrix for predictive modelling; Task matrix for predictive modelling; Applications and examples of predictive modelling; LinkedIn's ""People also viewed"" feature; What it does?; How is it done?
Correct targeting of online adsHow is it done?; Santa Cruz predictive policing; How is it done?; Determining the activity of a smartphone user using accelerometer data; How is it done?; Sport and fantasy leagues; How was it done?; Python and its packages - download and installation; Anaconda; Standalone Python; Installing a Python package; Installing pip; Installing Python packages with pip; Python and its packages for predictive modelling; IDEs for Python; Summary; Chapter 2: Data Cleaning ; Reading the data - variations and examples; Data frames; Delimiters Various methods of importing data in PythonCase 1 - reading a dataset using the read_csv method; The read_csv method; Use cases of the read_csv method; Case 2 - reading a dataset using the open method of Python; Reading a dataset line by line; Changing the delimiter of a dataset; Case 3 - reading data from a URL; Case 4 - miscellaneous cases; Reading from an .xls or .xlsx file; Writing to a CSV or Excel file; Basics - summary, dimensions, structure; Handling missing values; Checking for missing values; What constitutes missing data?; How missing values are generated and propagated Treating missing valuesDeletion; Imputation; Creating dummy variables; Visualizing a dataset by basic plotting; Scatter plots; Histograms; Boxplots; Summary; Chapter 3: Data Wrangling ; Subsetting a dataset; Selecting columns; Selecting rows; Selecting a combination of rows and columns; Creating new columns; Generating random numbers and their usage; Various methods for generating random numbers; Seeding a random number; Generating random numbers following probability distributions; Probability density function; Cumulative density function; Uniform distribution; Normal distribution Using the Monte-Carlo simulation to find the value of piGeometry and mathematics behind the calculation of pi; Generating a dummy data frame; Grouping the data - aggregation, filtering, and transformation; Aggregation; Filtering; Transformation; Miscellaneous operations; Random sampling - splitting a dataset in training and testing datasets; Method 1 - using the Customer Churn Model; Method 2 - using sklearn; Method 3 - using the shuffle function; Concatenating and appending data; Merging/joining datasets; Inner Join; Left Join; Right Join; An example of the Inner Join An example of the Left Join |
| Record Nr. | UNINA-9910798172103321 |
|
Kumar Ashish (Analyst)
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| Birmingham : , : Packt Publishing, , 2016 | ||
| Materiale a stampa | ||
| Lo trovi qui: Univ. Federico II | ||
| ||
Learning predictive analytics with Python : gain practical insights into predictive modelling by implementing predictive analytics algorithms on public datasets with Python / / Ashish Kumar ; [foreword by Pradeep Gulipalli, co-founder and head of India operations - Tiger Analytics]
| Learning predictive analytics with Python : gain practical insights into predictive modelling by implementing predictive analytics algorithms on public datasets with Python / / Ashish Kumar ; [foreword by Pradeep Gulipalli, co-founder and head of India operations - Tiger Analytics] |
| Autore | Kumar Ashish (Analyst) |
| Edizione | [First edition] |
| Pubbl/distr/stampa | Birmingham : , : Packt Publishing, , 2016 |
| Descrizione fisica | 1 online resource (354 pages) |
| Collana | Community experience distilled |
| Soggetto topico |
Python (Computer program language)
R (Computer program language) Decision making - Statistical methods Forecasting - Mathematical models |
| ISBN | 1-78398-327-2 |
| Formato | Materiale a stampa |
| Livello bibliografico | Monografia |
| Lingua di pubblicazione | eng |
| Nota di contenuto |
Cover ; Copyright; Credits; Foreword; About the Author; Acknowledgments; About the Reviewer; www.PacktPub.com; Table of Contents; Preface; Chapter 1: Getting Started with Predictive Modelling ; Introducing predictive modelling; Scope of predictive modelling; Ensemble of statistical algorithms; Statistical tools; Historical data; Mathematical function; Business context; Knowledge matrix for predictive modelling; Task matrix for predictive modelling; Applications and examples of predictive modelling; LinkedIn's ""People also viewed"" feature; What it does?; How is it done?
Correct targeting of online adsHow is it done?; Santa Cruz predictive policing; How is it done?; Determining the activity of a smartphone user using accelerometer data; How is it done?; Sport and fantasy leagues; How was it done?; Python and its packages - download and installation; Anaconda; Standalone Python; Installing a Python package; Installing pip; Installing Python packages with pip; Python and its packages for predictive modelling; IDEs for Python; Summary; Chapter 2: Data Cleaning ; Reading the data - variations and examples; Data frames; Delimiters Various methods of importing data in PythonCase 1 - reading a dataset using the read_csv method; The read_csv method; Use cases of the read_csv method; Case 2 - reading a dataset using the open method of Python; Reading a dataset line by line; Changing the delimiter of a dataset; Case 3 - reading data from a URL; Case 4 - miscellaneous cases; Reading from an .xls or .xlsx file; Writing to a CSV or Excel file; Basics - summary, dimensions, structure; Handling missing values; Checking for missing values; What constitutes missing data?; How missing values are generated and propagated Treating missing valuesDeletion; Imputation; Creating dummy variables; Visualizing a dataset by basic plotting; Scatter plots; Histograms; Boxplots; Summary; Chapter 3: Data Wrangling ; Subsetting a dataset; Selecting columns; Selecting rows; Selecting a combination of rows and columns; Creating new columns; Generating random numbers and their usage; Various methods for generating random numbers; Seeding a random number; Generating random numbers following probability distributions; Probability density function; Cumulative density function; Uniform distribution; Normal distribution Using the Monte-Carlo simulation to find the value of piGeometry and mathematics behind the calculation of pi; Generating a dummy data frame; Grouping the data - aggregation, filtering, and transformation; Aggregation; Filtering; Transformation; Miscellaneous operations; Random sampling - splitting a dataset in training and testing datasets; Method 1 - using the Customer Churn Model; Method 2 - using sklearn; Method 3 - using the shuffle function; Concatenating and appending data; Merging/joining datasets; Inner Join; Left Join; Right Join; An example of the Inner Join An example of the Left Join |
| Record Nr. | UNINA-9910809979003321 |
|
Kumar Ashish (Analyst)
|
||
| Birmingham : , : Packt Publishing, , 2016 | ||
| Materiale a stampa | ||
| Lo trovi qui: Univ. Federico II | ||
| ||
Revisiting the extent to which payroll taxes are passed through to employees / / Dorian Carloni
| Revisiting the extent to which payroll taxes are passed through to employees / / Dorian Carloni |
| Autore | Carloni Dorian |
| Pubbl/distr/stampa | Washington, D.C. : , : Congressional Budget Office, , 2021 |
| Descrizione fisica | 1 online resource (58 pages, 3 unnumbered pages) : color illustrations |
| Collana | Working paper |
| Soggetto topico |
Payroll tax - United States
Forecasting - Mathematical models Economic forecasting - United States |
| Formato | Materiale a stampa |
| Livello bibliografico | Monografia |
| Lingua di pubblicazione | eng |
| Record Nr. | UNINA-9910716840703321 |
|
Carloni Dorian
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| Washington, D.C. : , : Congressional Budget Office, , 2021 | ||
| Materiale a stampa | ||
| Lo trovi qui: Univ. Federico II | ||
| ||
Understanding the predictive analytics lifecycle / / Alberto Cordoba
| Understanding the predictive analytics lifecycle / / Alberto Cordoba |
| Autore | Cordoba Alberto <1958-> |
| Edizione | [1st edition] |
| Pubbl/distr/stampa | Hoboken, New Jersey : , : Wiley, , 2014 |
| Descrizione fisica | 1 online resource (240 pages) |
| Disciplina | 658.4/013 |
| Collana | Wiley & SAS Business Series |
| Soggetto topico |
Decision making - Statistical methods
Forecasting - Mathematical models Business planning |
| ISBN |
1-118-93893-3
1-118-93674-4 |
| Classificazione | BUS019000 |
| Formato | Materiale a stampa |
| Livello bibliografico | Monografia |
| Lingua di pubblicazione | eng |
| Nota di contenuto | Machine generated contents note: Foreword Preface Acknowledgments Chapter 1 Problem Identification and Definition Importance of Clear Business Objectives Office Politics Note Chapter 2 Design and Build The Managing Phase The Planning Phase The Delivery Phase Notes Chapter 3 Data Acquisition Data: the Fuel for Analytics A Data Scientist's Job Notes Chapter 4 Exploration and Reporting Visualization Cloud Reporting Chapter 5 Modeling Churn Model Risk Scoring Model Notes Chapter 6 Actionable Analytics Digital Asset Management Social Media Chapter 7 Feedback What the Different Software Components Should Do Note Conclusion Appendix: Useful Questions Bibliography About the Author Index . |
| Record Nr. | UNINA-9910208959703321 |
|
Cordoba Alberto <1958->
|
||
| Hoboken, New Jersey : , : Wiley, , 2014 | ||
| Materiale a stampa | ||
| Lo trovi qui: Univ. Federico II | ||
| ||
Understanding the predictive analytics lifecycle / / Alberto Cordoba
| Understanding the predictive analytics lifecycle / / Alberto Cordoba |
| Autore | Cordoba Alberto <1958-> |
| Edizione | [1st edition] |
| Pubbl/distr/stampa | Hoboken, New Jersey : , : Wiley, , 2014 |
| Descrizione fisica | 1 online resource (240 pages) |
| Disciplina | 658.4/013 |
| Collana | Wiley & SAS Business Series |
| Soggetto topico |
Decision making - Statistical methods
Forecasting - Mathematical models Business planning |
| ISBN |
1-118-93893-3
1-118-93674-4 |
| Classificazione | BUS019000 |
| Formato | Materiale a stampa |
| Livello bibliografico | Monografia |
| Lingua di pubblicazione | eng |
| Nota di contenuto | Machine generated contents note: Foreword Preface Acknowledgments Chapter 1 Problem Identification and Definition Importance of Clear Business Objectives Office Politics Note Chapter 2 Design and Build The Managing Phase The Planning Phase The Delivery Phase Notes Chapter 3 Data Acquisition Data: the Fuel for Analytics A Data Scientist's Job Notes Chapter 4 Exploration and Reporting Visualization Cloud Reporting Chapter 5 Modeling Churn Model Risk Scoring Model Notes Chapter 6 Actionable Analytics Digital Asset Management Social Media Chapter 7 Feedback What the Different Software Components Should Do Note Conclusion Appendix: Useful Questions Bibliography About the Author Index . |
| Record Nr. | UNINA-9910823865003321 |
|
Cordoba Alberto <1958->
|
||
| Hoboken, New Jersey : , : Wiley, , 2014 | ||
| Materiale a stampa | ||
| Lo trovi qui: Univ. Federico II | ||
| ||
