Estimating online ad effectiveness
|
|
|
- Crystal Douglas
- 5 years ago
- Views:
Transcription
1 Estimating online ad effectiveness Kay H. Brodersen Hal R. Varian Google,Inc. January 7, 2015
2 A note about the paper Created using knitr Allows you to merge L A TEXand R commands Runs R each time you change paper and caches results Inserts R code and output into paper Voila: reproducible research
3 Estimating online ad effectiveness 1. Apply treatment: change ad spend, bid, budget, etc. 1.1 Treat in some places but not others 1.2 Treat in some times but not others 2. Compare to counterfactual: what would have happened without experiment? 3. Of course, counterfactual can not be observed, so it must be estimated
4 Procedure Train a model to predict metric of interest such as website visits or revenue. Test the model on out-of-sample data to evaluate performance. Treat Apply model to periods or places where treatment was applied to estimate counterfactual Compare actual and (estimated) counterfactual outcomes.
5 Hypothetical example of train-test-treat visits Train Test Treat Test
6 Bayesian Structural Time Series We will do this in a time series context using BSTS (available from CRAN.) BSTS combines: Kalman filter. Accounts for seasonality and trend Spike-and-slab regression. Automated selection of predictors Bayesian model averaging. Avoids overfitting. Described in Scott-Varian [2013,2014], Brodersen et. al. [2013]. Related to interrupted regression, synthetic controls.
7 Predictors selected by BSTS Computers_and_Electronics Electronics_and_Electrical Shopping Photo_and_Video_Services Internet_and_Telecom Service_Providers Health Vision_Care Law_and_Government Public_Safety Inclusion Probability
8 Prediction from BSTS cbind(y, y.pred) Actual Counterfactual Interval Jun 15 Jul 01 Jul 15 Aug 01 Aug 15 Sep 01
9 Research strategy? 1. Use a parametric model for impact of ad campaign? Benefit: Can use all the data to estimate Cost: Restrictive functional form: a parallel shift 2. Use alternative estimation technique? 3. Use different models for seasonality and trend
10 1. Use parallel shift for ad impact Boxcar variable pred error Jun 15 Jul 01 Jul 15 Aug 01 Aug 15 Sep 01 Index
11 Boxcar indicator variable for campaign campaign Shopping Photo_and_Video_Services Online_Communities File_Sharing_and_Hosting Sports Computers_and_Electronics Electronics_and_Electrical
12 2. Alternative estimation: linear model Drop Kalman filter, just use simple linear model July 4 holiday dummy Top two categories from Google Trends as regressors cbind(y, y.pred, y.counterfactual) Actual Predicted Counterfactual Jun 15 Jul 01 Jul 15 Aug 01 Aug 15 Sep 01
13 Deseasonalized the data first Deseasonalize by fitting model with holiday regressor + day-of-week dummies. (Explain spike.) y.ds Jun 15 Jul 01 Jul 15 Aug 01 Aug 15 Sep 01
14 Alternative approaches 1. Make no adjustment for seasonality (since predictor already has appropriate seasonality) 2. Deseasonalize both predictor and outcome Use boxcar regressor Use extrapolation
15 3. Alternative seasonality: detrend first Deseasonalize by fitting model with holiday regressor + day of week dummies. (Explain spike.) cbind(y.ds, y.pred) Actual Counterfactual Jun 15 Jul 01 Jul 15 Aug 01 Aug 15 Sep 01
16 Use weekly data cbind(y.week, y.pred1, y.pred2) actual boxcar photo Jun 15 Jul 01 Jul 15 Aug 01 Aug 15
17 Summary method estimate 1 bsts-extrap bsts-boxcar bsts-boxcar-all-predictors bsts-boxcar-top-predictors lm-boxcar lm-extrap not deseasonalized deseasonalized-boxcar deseasonalized-extrap week-boxcar
18 What about revenue? Ad clicks may cannibalize search clicks May want to look at total number of clicks (i.e., visitors) But ad clicks may be worth more or less than search clicks, so really want revenue (or profit) Can model ad revenue, search revenue separately or together Examine a different advertiser using a different set of possible predictors
19 BSTS: predictor selection for visits campaign Movie.Rentals...Sales.Clicks Movie.Rentals...Sales.Impressions Movie.Rentals...Sales.Matched Movie.Rentals...Sales.Queries
20 BSTS: Visits actual and counterfactual Uses the BSTS extrapolation model and a linear regression cbind(y, y.pred.top boxcar, y.pred.bsts) Actual Linear model BSTS Jan 14 Jan 21 Jan 28 Feb 04 Feb 11 Feb 18 Index
21 BSTS: predictor selection for revenue campaign Movie.Rentals...Sales.Clicks Movie.Rentals...Sales.Impressions Music.Clicks Movie.Rentals...Sales.Revenue
22 BSTS: Revenue actual and counterfactual Uses the BSTS extrapolation model and a linear regression cbind(y, y.pred.top boxcar, y.pred.bsts) Actual Linear model Structural time series Jan 14 Jan 21 Jan 28 Feb 04 Feb 11 Feb 18 Index
23 Revenue cannibalization When campaign begins ad revenue increases significantly, organic revenue falls somewhat. cbind(search, ads) ads search Jan 14 Jan 21 Jan 28 Feb 04 Feb 11 Feb 18 Index
Bayesian Variable Selection for Nowcasting Time Series
Bayesian Variable Selection for Time Series Steve Scott Hal Varian Google August 14, 2013 What day of the week are there the most searches for [hangover]? 1. Sunday 2. Monday 3. Tuesday 4. Wednesday 5.
GAMINGRE 8/1/ of 7
FYE 09/30/92 JULY 92 0.00 254,550.00 0.00 0 0 0 0 0 0 0 0 0 254,550.00 0.00 0.00 0.00 0.00 254,550.00 AUG 10,616,710.31 5,299.95 845,656.83 84,565.68 61,084.86 23,480.82 339,734.73 135,893.89 67,946.95
Multiple Regression Analysis
1 OUTLINE Analysis of Data and Model Hypothesis Testing Dummy Variables Research in Finance 2 ANALYSIS: Types of Data Time Series data Cross-Sectional data Panel data Trend Seasonal Variation Cyclical
DEPARTMENT OF THE ARMY MILITARY SURFACE DEPLOYMENT AND DISTRIBUTION COMMAND (SDDC) 1 SOLDIER WAY SCOTT AFB, IL 62225
DEPARTMENT OF THE ARMY MILITARY SURFACE DEPLOYMENT AND DISTRIBUTION COMMAND (SDDC) 1 SOLDIER WAY SCOTT AFB, IL 62225 SDDC Operations Special Requirements Branch 1 Soldier Way Scott AFB, IL 62225 April
Lecture Prepared By: Mohammad Kamrul Arefin Lecturer, School of Business, North South University
Lecture 15 20 Prepared By: Mohammad Kamrul Arefin Lecturer, School of Business, North South University Modeling for Time Series Forecasting Forecasting is a necessary input to planning, whether in business,
Determine the trend for time series data
Extra Online Questions Determine the trend for time series data Covers AS 90641 (Statistics and Modelling 3.1) Scholarship Statistics and Modelling Chapter 1 Essent ial exam notes Time series 1. The value
STATISTICAL FORECASTING and SEASONALITY (M. E. Ippolito; )
STATISTICAL FORECASTING and SEASONALITY (M. E. Ippolito; 10-6-13) PART I OVERVIEW The following discussion expands upon exponential smoothing and seasonality as presented in Chapter 11, Forecasting, in
Lecture Prepared By: Mohammad Kamrul Arefin Lecturer, School of Business, North South University
Lecture 15 20 Prepared By: Mohammad Kamrul Arefin Lecturer, School of Business, North South University Modeling for Time Series Forecasting Forecasting is a necessary input to planning, whether in business,
Time Series Analysis
Time Series Analysis A time series is a sequence of observations made: 1) over a continuous time interval, 2) of successive measurements across that interval, 3) using equal spacing between consecutive
peak half-hourly New South Wales
Forecasting long-term peak half-hourly electricity demand for New South Wales Dr Shu Fan B.S., M.S., Ph.D. Professor Rob J Hyndman B.Sc. (Hons), Ph.D., A.Stat. Business & Economic Forecasting Unit Report
In Centre, Online Classroom Live and Online Classroom Programme Prices
In Centre, and Online Classroom Programme Prices In Centre Online Classroom Foundation Certificate Bookkeeping Transactions 430 325 300 Bookkeeping Controls 320 245 225 Elements of Costing 320 245 225
M I C R O C O N V E R S I O N G O A L S
C O N T A C T U S T h i s p a g e l i s t s a l l o f t h e d i f f e r e n t d e p a r t m e n t s t o c o n t a c t, d e p e n d i n g o n y o u r r e q u e s t. A t t h e b o t t o m o f t h e p a g
ACCA Interactive Timetable
ACCA Interactive Timetable 2018 Professional Version 7.1 Information last updated 15th May 2018 Please note: Information and dates in this timetable are subject to change. A better way of learning that
Time series and Forecasting
Chapter 2 Time series and Forecasting 2.1 Introduction Data are frequently recorded at regular time intervals, for instance, daily stock market indices, the monthly rate of inflation or annual profit figures.
ACCA Interactive Timetable & Fees
ACCA Interactive Timetable & Fees 2018/19 Professional Version 2.1 Information last updated uary 2019 Please note: Information and dates in this timetable are subject to change. A better way of learning
d. Predict the number of photos this photographer will keep if she takes 200 photos.
1 Solve. Regression Practice Curve Fitting with Linear Models 1. Vern created a website about his school s sports teams. He has a hit counter on his site that lets him know how many people have visited
ACCA Interactive Timetable
ACCA Interactive Timetable 2018 Professional Version 3.1 Information last updated 1st May 2018 Book Please online note: atinformation and dates in this timetable are subject Or to change. call -enrol A
ACCA Interactive Timetable & Fees
ACCA Interactive Timetable & Fees 2018/19 Professional Version 2.1 Information last updated 01 November 2018 Please note: Information and dates in this timetable are subject to change. A better way of
Computing & Telecommunications Services Monthly Report January CaTS Help Desk. Wright State University (937)
January 215 Monthly Report Computing & Telecommunications Services Monthly Report January 215 CaTS Help Desk (937) 775-4827 1-888-775-4827 25 Library Annex helpdesk@wright.edu www.wright.edu/cats/ Last
Whitby Community College Your account expires on: 8 Nov, 2015
To print higher resolution math symbols, click the Hi Res Fonts for Printing button on the jsmath control panel. If the math symbols print as black boxes, turn off image alpha channels using the Options
BUSI 460 Suggested Answers to Selected Review and Discussion Questions Lesson 7
BUSI 460 Suggested Answers to Selected Review and Discussion Questions Lesson 7 1. The definitions follow: (a) Time series: Time series data, also known as a data series, consists of observations on a
ACCA Interactive Timetable & Fees
ACCA Interactive Timetable & Fees 2018/19 Professional Version 1.1 Information last updated tember 2018 Please note: Information and dates in this timetable are subject to change. A better way of learning
ACCA Interactive Timetable & Fees
ACCA Interactive Timetable & Fees 2018/19 Professional Version 1.1 Information last updated tember 2018 Please note: Information and dates in this timetable are subject to change. A better way of learning
Tools for Google Data. Hal Varian Google July 2015
Tools for Google Data Hal Varian Google July 2015 1 Google Trends Google Correlate Google Consumer Surveys 2 Searches for [hangover] Which day of the week are there the most searches for [hangover]? 1:
ACCA Interactive Timetable & Fees
ACCA Interactive Timetable & Fees 2018/19 Professional Version 1.1 Information last updated tember 2018 Please note: Information and dates in this timetable are subject to change. A better way of learning
DOZENALS. A project promoting base 12 counting and measuring. Ideas and designs by DSA member (#342) and board member, Timothy F. Travis.
R AENBO DOZENALS A project promoting base 12 counting and measuring. Ideas and designs by DSA member (#342) and board member Timothy F. Travis. I became aware as a teenager of base twelve numbering from
ACCA Interactive Timetable & Fees
ACCA Interactive Timetable & Fees 2018 Professional Version 4.1 Information last updated 11th September 2018 Please note: Information and dates in this timetable are subject to change. A better way of
ACCA Interactive Timetable & Fees
ACCA Interactive Timetable & Fees 2018/19 Professional London Version 1.1 Information last updated 3 October 2018 Please note: Information and dates in this timetable are subject to change. A better way
peak half-hourly Tasmania
Forecasting long-term peak half-hourly electricity demand for Tasmania Dr Shu Fan B.S., M.S., Ph.D. Professor Rob J Hyndman B.Sc. (Hons), Ph.D., A.Stat. Business & Economic Forecasting Unit Report for
CIMA Professional 2018
CIMA Professional 2018 Interactive Timetable Version 16.25 Information last updated 06/08/18 Please note: Information and dates in this timetable are subject to change. A better way of learning that s
ACCA Interactive Timetable & Fees
ACCA Interactive Timetable & Fees 2018/19 Professional Milton Keynes Version 1.1 Information last updated tember 2018 Please note: Information and dates in this timetable are subject to change. A better
ACCA Interactive Timetable
ACCA Interactive Timetable 2018 Professional Version 5.1 Information last updated 2nd May 2018 Please note: Information and dates in this timetable are subject to change. A better way of learning that
CIMA Professional
CIMA Professional 201819 Birmingham Interactive Timetable Version 3.1 Information last updated 12/10/18 Please note: Information and dates in this timetable are subject to change. A better way of learning
ACCA Interactive Timetable & Fees
ACCA Interactive Timetable & Fees 2018/19 Professional Version 3.1 Information last updated 1st May 2018 Please note: Information and dates in this timetable are subject to change. A better way of learning
CIMA Professional
CIMA Professional 201819 Manchester Interactive Timetable Version 3.1 Information last updated 12/10/18 Please note: Information and dates in this timetable are subject to change. A better way of learning
ACCA Interactive Timetable
ACCA Interactive Timetable 2018 Professional Version 9.1 Information last updated 18 July 2018 Please note: Information and dates in this timetable are subject to change. A better way of learning that
CIMA Professional 2018
CIMA Professional 2018 Newcastle Interactive Timetable Version 10.20 Information last updated 12/06/18 Please note: Information and dates in this timetable are subject to change. A better way of learning
Predicting Long-term Exposures for Health Effect Studies
Predicting Long-term Exposures for Health Effect Studies Lianne Sheppard Adam A. Szpiro, Johan Lindström, Paul D. Sampson and the MESA Air team University of Washington CMAS Special Session, October 13,
Technical note on seasonal adjustment for M0
Technical note on seasonal adjustment for M0 July 1, 2013 Contents 1 M0 2 2 Steps in the seasonal adjustment procedure 3 2.1 Pre-adjustment analysis............................... 3 2.2 Seasonal adjustment.................................
ACCA Interactive Timetable
ACCA Interactive Timetable 2018 Professional Version 3.1 Information last updated 1st May 2018 Please note: Information and dates in this timetable are subject to change. A better way of learning that
Technical note on seasonal adjustment for Capital goods imports
Technical note on seasonal adjustment for Capital goods imports July 1, 2013 Contents 1 Capital goods imports 2 1.1 Additive versus multiplicative seasonality..................... 2 2 Steps in the seasonal
ACCA Interactive Timetable & Fees
ACCA Interactive Timetable & Fees 2018/19 Professional Version 1.1 Information last updated tember 2018 Please note: Information and dates in this timetable are subject to change. A better way of learning
W E E K L Y P U B L I C AT I O N. Commodities Weekly
W E E K L Y P U B L I C AT I O N Commodities Weekly For further information or questions, please email: mike.m@xatsuk.com Website https:/// DISCLAIMER: By using this report, you accept this disclaimer
Practice Test Chapter 8 Sinusoidal Functions
FOM 12 Practice Test Chapter 8 Sinusoidal Functions Name: Multiple Choice Identify the choice that best completes the statement or answers the question. Block: _ 1. Convert 120 into radians. A. 2" 3 B.
2018 Annual Review of Availability Assessment Hours
2018 Annual Review of Availability Assessment Hours Amber Motley Manager, Short Term Forecasting Clyde Loutan Principal, Renewable Energy Integration Karl Meeusen Senior Advisor, Infrastructure & Regulatory
DAILY QUESTIONS 28 TH JUNE 18 REASONING - CALENDAR
DAILY QUESTIONS 28 TH JUNE 18 REASONING - CALENDAR LEAP AND NON-LEAP YEAR *A non-leap year has 365 days whereas a leap year has 366 days. (as February has 29 days). *Every year which is divisible by 4
Advances in promotional modelling and analytics
Advances in promotional modelling and analytics High School of Economics St. Petersburg 25 May 2016 Nikolaos Kourentzes n.kourentzes@lancaster.ac.uk O u t l i n e 1. What is forecasting? 2. Forecasting,
Suan Sunandha Rajabhat University
Forecasting Exchange Rate between Thai Baht and the US Dollar Using Time Series Analysis Kunya Bowornchockchai Suan Sunandha Rajabhat University INTRODUCTION The objective of this research is to forecast
Bayesian Machine Learning in Finance
Bayesian Machine Learning in Finance Patricia(Ning) Ning Computational Finance & Risk Management Dept. of Applied Math Univ. of Washington, Seattle ningnin@uw.edu Talk maily based on paper Multivariate
ACCA Interactive Timetable
ACCA Interactive Timetable 2018 Professional Version 4.1 Information last updated 1st May 2018 Please note: Information and dates in this timetable are subject to change. A better way of learning that
ACCA Interactive Timetable
ACCA Interactive Timetable 2018 Professional information last updated uary 2018 v4 Please note: Information and dates in this timetable are subject to change. How the 4 exam sittings can work for you ACCA
Highlights of the 2006 Water Year in Colorado
Highlights of the 2006 Water Year in Colorado Nolan Doesken, State Climatologist Atmospheric Science Department Colorado State University http://ccc.atmos.colostate.edu Presented to 61 st Annual Meeting
ACCA Interactive Timetable
ACCA Interactive Timetable 2018 Professional Version 6.1 Information last updated 6th June 2018 Please note: Information and dates in this timetable are subject to change. A better way of learning that
Computing & Telecommunications Services
Computing & Telecommunications Services Monthly Report September 214 CaTS Help Desk (937) 775-4827 1-888-775-4827 25 Library Annex helpdesk@wright.edu www.wright.edu/cats/ Table of Contents HEAT Ticket
ACCA Interactive Timetable
ACCA Interactive Timetable 2018 Professional information last updated 4 April 2018 v3.1 Please note: Information and dates in this timetable are subject to change. How the 4 exam sittings can work for
Winter Season Resource Adequacy Analysis Status Report
Winter Season Resource Adequacy Analysis Status Report Tom Falin Director Resource Adequacy Planning Markets & Reliability Committee October 26, 2017 Winter Risk Winter Season Resource Adequacy and Capacity
ACCA Interactive Timetable
ACCA Interactive Timetable 2018 Professional Version 3.1 Information last updated 1st May 2018 Please note: Information and dates in this timetable are subject to change. A better way of learning that
Mr. XYZ. Stock Market Trading and Investment Astrology Report. Report Duration: 12 months. Type: Both Stocks and Option. Date: Apr 12, 2011
Mr. XYZ Stock Market Trading and Investment Astrology Report Report Duration: 12 months Type: Both Stocks and Option Date: Apr 12, 2011 KT Astrologer Website: http://www.softwareandfinance.com/magazine/astrology/kt_astrologer.php
MEDIA KIT 2017 CANADIANS. CONNECT with our members today! CANADIAN PHYSIOTHERAPY ASSOCIATION
CANADIAN PHYSIOTHERAPY ASSOCIATION MEDIA KIT CANADIANS a r e MOVING m o v i n g MORE a n d m o v i n g BETTER with the help of PHYSIOTHERAPY Physiotherapists are improving the health of Canadians everyday
SYSTEM BRIEF DAILY SUMMARY
SYSTEM BRIEF DAILY SUMMARY * ANNUAL MaxTemp NEL (MWH) Hr Ending Hr Ending LOAD (PEAK HOURS 7:00 AM TO 10:00 PM MON-SAT) ENERGY (MWH) INCREMENTAL COST DAY DATE Civic TOTAL MAXIMUM @Max MINIMUM @Min FACTOR
Lesson Adaptation Activity: Analyzing and Interpreting Data
Lesson Adaptation Activity: Analyzing and Interpreting Data Related MA STE Framework Standard: 3-ESS2-1. Use graphs and tables of local weather data to describe and predict typical weather during a particular
INFERRING CAUSAL IMPACT USING BAYESIAN STRUCTURAL TIME-SERIES MODELS
The Annals of Applied Statistics 2015, Vol. 9, No. 1, 247 274 DOI: 10.1214/14-AOAS788 Institute of Mathematical Statistics, 2015 INFERRING CAUSAL IMPACT USING BAYESIAN STRUCTURAL TIME-SERIES MODELS BY
Introduction to Forecasting
Introduction to Forecasting Introduction to Forecasting Predicting the future Not an exact science but instead consists of a set of statistical tools and techniques that are supported by human judgment
AIT Graduation (Pinning Ceremony) 1-78th Field Artillery
13B10 CANNON CREWMEMBER 13D10 FA TACTICAL DATA SYS SPECIALIST 16-10 12 FEB 10 26 MAR 10 21-10 12 FEB 10 6 APR 10 18-10 19 FEB 10 1 APR 10 22-10 12 FEB 10 6 APR 10 19-10 19 MAR 10 29 APR 10 23-10 19 FEB
Math Final Exam Review. 1. The following equation gives the rate at which the angle between two objects is changing during a game:
Math 131 Spring 2008 c Sherry Scarborough and Heather Ramsey Page 1 Math 131 - Final Exam Review 1. The following equation gives the rate at which the angle between two objects is changing during a game:
Improve Forecasts: Use Defect Signals
Improve Forecasts: Use Defect Signals Paul Below paul.below@qsm.com Quantitative Software Management, Inc. Introduction Large development and integration project testing phases can extend over many months
Atmospheric circulation analysis for seasonal forecasting
Training Seminar on Application of Seasonal Forecast GPV Data to Seasonal Forecast Products 18 21 January 2011 Tokyo, Japan Atmospheric circulation analysis for seasonal forecasting Shotaro Tanaka Climate
YEAR 10 GENERAL MATHEMATICS 2017 STRAND: BIVARIATE DATA PART II CHAPTER 12 RESIDUAL ANALYSIS, LINEARITY AND TIME SERIES
YEAR 10 GENERAL MATHEMATICS 2017 STRAND: BIVARIATE DATA PART II CHAPTER 12 RESIDUAL ANALYSIS, LINEARITY AND TIME SERIES This topic includes: Transformation of data to linearity to establish relationships
YACT (Yet Another Climate Tool)? The SPI Explorer
YACT (Yet Another Climate Tool)? The SPI Explorer Mike Crimmins Assoc. Professor/Extension Specialist Dept. of Soil, Water, & Environmental Science The University of Arizona Yes, another climate tool for
P7.7 A CLIMATOLOGICAL STUDY OF CLOUD TO GROUND LIGHTNING STRIKES IN THE VICINITY OF KENNEDY SPACE CENTER, FLORIDA
P7.7 A CLIMATOLOGICAL STUDY OF CLOUD TO GROUND LIGHTNING STRIKES IN THE VICINITY OF KENNEDY SPACE CENTER, FLORIDA K. Lee Burns* Raytheon, Huntsville, Alabama Ryan K. Decker NASA, Marshall Space Flight
Multiple Regression Analysis
1 OUTLINE Basic Concept: Multiple Regression MULTICOLLINEARITY AUTOCORRELATION HETEROSCEDASTICITY REASEARCH IN FINANCE 2 BASIC CONCEPTS: Multiple Regression Y i = β 1 + β 2 X 1i + β 3 X 2i + β 4 X 3i +
SYSTEM BRIEF DAILY SUMMARY
SYSTEM BRIEF DAILY SUMMARY * ANNUAL MaxTemp NEL (MWH) Hr Ending Hr Ending LOAD (PEAK HOURS 7:00 AM TO 10:00 PM MON-SAT) ENERGY (MWH) INCREMENTAL COST DAY DATE Civic TOTAL MAXIMUM @Max MINIMUM @Min FACTOR
Salem Economic Outlook
Salem Economic Outlook November 2012 Tim Duy, PHD Prepared for the Salem City Council November 7, 2012 Roadmap US Economic Update Slow and steady Positives: Housing/monetary policy Negatives: Rest of world/fiscal
Menstrual Lunar Calendar 2015 By Antolin READ ONLINE
Menstrual Lunar Calendar 2015 By Antolin READ ONLINE If searching for the book by Antolin Menstrual lunar calendar 2015 in pdf form, then you have come on to the loyal website. We present the utter release
Jayalath Ekanayake Jonas Tappolet Harald Gall Abraham Bernstein. Time variance and defect prediction in software projects: additional figures
Jayalath Ekanayake Jonas Tappolet Harald Gall Abraham Bernstein TECHNICAL REPORT No. IFI-2.4 Time variance and defect prediction in software projects: additional figures 2 University of Zurich Department
Four Basic Steps for Creating an Effective Demand Forecasting Process
Four Basic Steps for Creating an Effective Demand Forecasting Process Presented by Eric Stellwagen President & Cofounder Business Forecast Systems, Inc. estellwagen@forecastpro.com Business Forecast Systems,
A Report on a Statistical Model to Forecast Seasonal Inflows to Cowichan Lake
A Report on a Statistical Model to Forecast Seasonal Inflows to Cowichan Lake Prepared by: Allan Chapman, MSc, PGeo Hydrologist, Chapman Geoscience Ltd., and Former Head, BC River Forecast Centre Victoria
Dummy Variables. Susan Thomas IGIDR, Bombay. 24 November, 2008
IGIDR, Bombay 24 November, 2008 The problem of structural change Model: Y i = β 0 + β 1 X 1i + ɛ i Structural change, type 1: change in parameters in time. Y i = α 1 + β 1 X i + e 1i for period 1 Y i =
Your World is not Red or Green. Good Practice in Data Display and Dashboard Design
Your World is not Red or Green Good Practice in Data Display and Dashboard Design References Tufte, E. R. (2). The visual display of quantitative information (2nd Ed.). Cheshire, CT: Graphics Press. Few,
Available online at ScienceDirect. Procedia Computer Science 72 (2015 )
Available online at www.sciencedirect.com ScienceDirect Procedia Computer Science 72 (2015 ) 630 637 The Third Information Systems International Conference Performance Comparisons Between Arima and Arimax
Variability of Reference Evapotranspiration Across Nebraska
Know how. Know now. EC733 Variability of Reference Evapotranspiration Across Nebraska Suat Irmak, Extension Soil and Water Resources and Irrigation Specialist Kari E. Skaggs, Research Associate, Biological
What is the difference between Weather and Climate?
What is the difference between Weather and Climate? Objective Many people are confused about the difference between weather and climate. This makes understanding the difference between weather forecasts
Chapter 3. Regression-Based Models for Developing Commercial Demand Characteristics Investigation
Chapter Regression-Based Models for Developing Commercial Demand Characteristics Investigation. Introduction Commercial area is another important area in terms of consume high electric energy in Japan.
2003 Water Year Wrap-Up and Look Ahead
2003 Water Year Wrap-Up and Look Ahead Nolan Doesken Colorado Climate Center Prepared by Odie Bliss http://ccc.atmos.colostate.edu Colorado Average Annual Precipitation Map South Platte Average Precipitation
Combat Control Association Inc
STATEMENT OF ACTIVITY January 1 - July 6, 2016 Total REVENUE Direct Public Support 28,307.50 Membership Dues Annual Membership Dues 180.00 Lifetime Membership Dues 1,950.00 Total Membership Dues 2,130.00
Status of the CWE Flow Based Market Coupling Project
Commissie voor de Regulering van de Elektriciteit en het Gas Commission pour la Régulation de l Electricité et du Gaz Status of the CWE Flow Based Market Coupling Project Joint NordREG / Nordic TSO workshop
Time Series Analysis of United States of America Crude Oil and Petroleum Products Importations from Saudi Arabia
International Journal of Applied Science and Technology Vol. 5, No. 5; October 2015 Time Series Analysis of United States of America Crude Oil and Petroleum Products Importations from Saudi Arabia Olayan
Helping the Red Cross predict flooding in Togo
Helping the Red Cross predict flooding in Togo Abstract- This project aims to help the Red Cross predict flooding occurrences in Togo due to overflow in the Nangbeto Dam. Flooding is a result of both high
ACCA Interactive Timetable
ACCA Interactive Timetable 2018 Professional information last updated uary 2018 v2.1 Please note: Information and dates in this timetable are subject to change. How the 4 exam sittings can work for you
Analysing The Temporal Pattern Of Crime Hotspots In South Yorkshire, United Kingdom
Advances in Social Sciences Research Journal Vol.5, No.6 Publication Date: June. 25, 218 DoI:1.14738/assrj.56.4822. Abdullahi, H., & Abbas, I. I. (218). Analysing The Temporal Pattern Of Crime Hotspots
Reuse, Recycle: How to Repurpose Your Content. and Make the Big Bucks
P O W E R Reuse, Recycle: How to Repurpose Your Content and Make the Big Bucks 1 2 AS H L E Y W AR D Corporate Speaker SEMrush I N T E R N A T I O N A L S P E A K E R W R I T E R F O R S E A R C H E N
Prairie Climate Centre Prairie Climate Atlas. Visualizing Climate Change Projections for the Canadian Prairie Provinces
Prairie Climate Centre Prairie Climate Atlas Visualizing Climate Change Projections for the Canadian Prairie Provinces Acknowledgements About Us Dr. Danny Blair Dr. Ian Mauro Ryan Smith, MSc Dr. Hank Venema
Time Series Analysis of Currency in Circulation in Nigeria
ISSN -3 (Paper) ISSN 5-091 (Online) Time Series Analysis of Currency in Circulation in Nigeria Omekara C.O Okereke O.E. Ire K.I. Irokwe O. Department of Statistics, Michael Okpara University of Agriculture
Forecasting. BUS 735: Business Decision Making and Research. exercises. Assess what we have learned
Forecasting BUS 735: Business Decision Making and Research 1 1.1 Goals and Agenda Goals and Agenda Learning Objective Learn how to identify regularities in time series data Learn popular univariate time
Smoothed Prediction of the Onset of Tree Stem Radius Increase Based on Temperature Patterns
Smoothed Prediction of the Onset of Tree Stem Radius Increase Based on Temperature Patterns Mikko Korpela 1 Harri Mäkinen 2 Mika Sulkava 1 Pekka Nöjd 2 Jaakko Hollmén 1 1 Helsinki
Biggest problem with data analytics. tons of data, not much insights.
Biggest problem with data analytics tons of data, not much insights. Mobile Marketing CAC KPI Advertisers Organic Which customer segment should we go after? Owned Media S E O What ad copy should I create?
Forecasting: principles and practice. Rob J Hyndman 1.1 Introduction to Forecasting
Forecasting: principles and practice Rob J Hyndman 1.1 Introduction to Forecasting 1 Outline 1 Background 2 Case studies 3 The statistical forecasting perspective 4 What can we forecast? 2 Resources Slides
BUDT 733 Data Analysis for Decision Makers
BUDT 733 Data Analysis for Decision Makers Fall 2007 - Sections DC01 Exploring A Trip to Hawaii What drives flight delays between DC and Honolulu by Prashant Bhaip Andres Garay Vedat Kaplan Greg Vinogradovg
The Cryosphere Radiative Effect in CESM. Justin Perket Mark Flanner CESM Polar Climate Working Group Meeting Wednesday June 19, 2013
The Cryosphere Radiative Effect in CESM Justin Perket Mark Flanner CESM Polar Climate Working Group Meeting Wednesday June 19, 2013 Cryosphere Radiative Effect (CrRE) A new diagnostic feature is available
Statistics 153 (Time Series) : Lecture Three
Statistics 153 (Time Series) : Lecture Three Aditya Guntuboyina 24 January 2012 1 Plan 1. In the last class, we looked at two ways of dealing with trend in time series data sets - Fitting parametric curves