Using Association Rule Mining: Stock Market Events Prediction from Financial News

Save this PDF as:

Size: px
Start display at page:

Download "Using Association Rule Mining: Stock Market Events Prediction from Financial News"

Transcription

4 Fgure1: System Archtecture 3.2 Assocaton Rule Mnng Generate Rules When techncal tradng values are collected then, the techncal tradng values for each day are modeled. Dependng upon the predcton date gven by the user the mappng s done and rules are generated. Each rule has one fact. For example If Share Prce= and SMA= then Sgnal=Buy. In ths Prce= and SMA= s a rule and Sgnal=Buy s a fact. Smlarly, for each day all techncal ndcators generate rules Facts Facts are nothng but the Buy/Sell/Hold sgnals generated by the rules. These facts gve the predcton to user whether to buy/sell/hold the shares. When we receve the multple facts from the multple rules the probablty of all facts are calculated and the correspondng sgnal s predcted to the user Generate sgnal After the rule mappng process the correspondng predcton for the shares.e. Sell/Buy/Hold s provded to the user. Accordng to ths predcton, the user decdes hs/her strategy. 3.3 The Nave Bayes Model Naïve Bayes classfer s used to tran the techncal ndcator. Rules whch are generated by usng all techncal ndcator values are traned by the Naïve Bayes. Bayesan classfer s based on Bayes theorem. Nave Bayesan classfers assume that the effect of techncal ndcator values on a gven class s ndependent of the values of the other techncal ndcator. Ths assumpton s called class condtonal ndependence. The nave Bayesan classfer works as follows: Let S be a tranng set of techncal ndcator lke SMA, BB, EMA, RoC, Momentum, MACD wth ther class labels and there are k classes, C 1, C 2, C 3..., C n. Each techncal ndcator s represented by an n-dmensonal vector, X=fx 1, x 2,...,x n depctng n measured values of the n attrbutes, A 1, A 2, A 3..., A n, respectvely. Gven a techncal ndcator X, the classfer wll predct that X belongs to the class havng the hghest probablty of the smlarty, condtoned on X. That s X s predcted to belong to the class C f and only f P (C X) > P (C X) for 1 m;. Bayes theorem: P C X = P X C P(C ) (7) P(X) Naïve Bayes Algorthm Learnng Phase: Gven a tranng set S, 1. For each target value of c (c c1,,c L ) Pˆ( C c ) estmate P( C c ) wth examples n S; 2. For every attrbute value a 3. Pˆ( X x ( 1,, n; k 1,,N ) a k of each attrbute C c ) estmate P( X C c ) wth examples n S; Output: condtonal probablty for elements x, N L Test Phase: Gven an unknown nstance X = (a 1,.., a n ) Look up to assgn the label c* to X f 4. Result and Dataset k a The proposed method can be evaluated n the context of two dfferent data sets collected from xgnte 1, mcxnda 2. 1) A closng prces of the shares at the end of the day takes from xgnte 1. 2) The another source s barchartondemand 2 whch s used for collecton of company name, closng prce of the shares closng prces of the shares. k Paper ID: SUB

5 Fgure 2: Graph of Share prces 1. gacy/1/getstckheadlnes Concluson Fgure 5: Fnal Sgnal The presented method for predcton of stock market consders only closng prces of the shares nstead of textual nformaton about the stocks. Ths reduces the efforts that are requred for the extracton of news nformaton. The tradng strateges consder techncal tradng ndcators, whch are used to generate the superor returns. The techncal tradng ndcator gve decson that s more approprate. Data mnng technque such as assocaton rule mmng and Naïve Bayes algorthm generates sgnfcant sgnals wthn the polynomal tme. It also ncreased the accuracy of the predcton system by acceptng the accurate closng prces of the stock. 6. Future Scope Fgure 3: Collected Share Prces The future work wll focus on ncludng more techncal ndcators whch wll generate the tradng strateges. The nteracton between events occurrng wthn the same day, or wthn fner tme ntervals wll be consdered. 7. Acknowledgment I express great many thanks to Prof. S.S.Nandgaonkar for her great effort of supervsng and leadng me, to accomplsh ths fne work. Also to college and department staff, they were a great source of support and encouragement. To my frends and famly, for ther warm, knd encourages and loves. To every person gave us somethng too lght my pathway, I thanks for belevng n me. References Fgure 4: Sgnal ganrataed by Smple Movng Avarege (SMA)Fgure 5:Fnal Predcted Sgnal [1] Sesa J. Zhao, Wagner, Chen Huapng, Revew of Predcton Market Research: Gudelnes for Informaton Systems Research [2] Hellström, T., Holmström, K., Predctng the Stock Market, Techncal Report Seres IMATOM , (1998). [3] Schoeneburg, E.(1990), Stock Prce Predcton Usng Neural Networks: A Proect Report, Neurocomputng, vol. 2, pp [4] Wuthrch, B., Permunetlleke, D., Leung, S., Cho, V., Zhang, J., Lam, W., Daly predcton of maor stock Paper ID: SUB

6 ndces from textual www data, n KDD, (1998), pp [5] Wnand Nu, Vorel Mlea, Frederk Hogenboom, An Automated Framework for Incorporatng News nto Stock Tradng Strateges IEEE Transactons on Knowledge and Data Engneerng, vol. 26, no. 4, aprl 2014 [6] Shubhang S. Umbarkar, Stock Market Predcton From Fnancal News: A survey, IJERGS vol. 06, ISSN [7] W. IJntema, J. Sangers, F. Hogenboom, and F. Frasncar, A Lexco-Semantc Pattern Language for Learnng Ontology Instances From Text, J. Web Semantcs: Scence, Servces and Agents on the World Wde Web, vol. 15, no. 1, pp , [8] M.-A. Mttermayer and G.F. Knolmayer, Text Mnng Systems for Market Response to News: A Survey, techncal report, Insttute of Informaton Systems Unversty of Bern. [9] Lavrenko, V.; Schmll, M.; Lawre, D.; Oglve, P.; Jensen, D.; Allan, J.: Mnng of Concurrent Text and Tme Seres. In: Proceedngs 6th ACM SIGKDD Int. Conference on Knowledge Dscovery and Data Mnng. Boston 2000, pp [10] Lavrenko, V.; Schmll, M.; Lawre, D.; Oglve, P.; Jensen, D.; Allan, J.: Language Models for Fnancal News Recommendaton. In: Proceedngs 9th Int. Conference on Informaton and Knowledge Management. Washngton 2000, pp [11] Oglve, P.; Schmll, M.: Ænalyst - Electronc Analyst of Stock Behavor. Proect Proposal 791m, Department of Computer Scence, Unversty of Massachusetts, Amherst. [12] Seo, Y.; Gampapa, J.A.; Sycara, K.: Text Classfcaton for Intellgent Portfolo Management. Techncal Report CMU-RI-TR-02-14, Robotcs Insttute, Carnege Mellon Unversty, Pttsburgh. [13] Seo, Y.; Gampapa, J.A.; Sycara, K.: Fnancal News Analyss for Intellgent Portfolo Management. Techncal Report CMU-RI-TR-04-04, Robotcs Insttute, Carnege Mellon Unversty, Pttsburgh. [14] F. Allen and R. Karalanen. \Usng Genetc Algorthms to Fnd Techncal Tradng Rules,"J. Economcs, vol. 51, no. 2, pp , [15] Puspanal Mohapatra, Alok Ra,Indan Stock Market Predcton Usng Dfferental Evolutonary Neural Network Model Internatonal Journal of Electroncs Communcaton and Computer Technology (IJECCT) Volume 2 Issue 4 (July 2012) Author Profle Shubhang S. Umbarkar. Receved her B.E. degree n Computer scence from unversty of Amravat n she s currently workng toward the M.E. Degree n Computer Engneerng from Unversty of Pune. She has attended number of workshops on Research Methodology, Cyber Securty, Latex, Sclab, Computer Vson etc. Also workshop on Image Processng, Computer Network, conducted by IIT, Bombay remote center at VPCOE, Baramat. Paper ID: SUB

The Development of Web Log Mining Based on Improve-K-Means Clustering Analysis

The Development of Web Log Mnng Based on Improve-K-Means Clusterng Analyss TngZhong Wang * College of Informaton Technology, Luoyang Normal Unversty, Luoyang, 471022, Chna wangtngzhong2@sna.cn Abstract.

Forecasting the Direction and Strength of Stock Market Movement

Forecastng the Drecton and Strength of Stock Market Movement Jngwe Chen Mng Chen Nan Ye cjngwe@stanford.edu mchen5@stanford.edu nanye@stanford.edu Abstract - Stock market s one of the most complcated systems

Course outline. Financial Time Series Analysis. Overview. Data analysis. Predictive signal. Trading strategy

Fnancal Tme Seres Analyss Patrck McSharry patrck@mcsharry.net www.mcsharry.net Trnty Term 2014 Mathematcal Insttute Unversty of Oxford Course outlne 1. Data analyss, probablty, correlatons, vsualsaton

Forecasting the Demand of Emergency Supplies: Based on the CBR Theory and BP Neural Network

700 Proceedngs of the 8th Internatonal Conference on Innovaton & Management Forecastng the Demand of Emergency Supples: Based on the CBR Theory and BP Neural Network Fu Deqang, Lu Yun, L Changbng School

, pp.395-400 http://dx.do.org/0.4257/astl.203.29.8 Identfyng Workloads n Mxed Applcatons Jeong Seok Oh, Hyo Jung Bang, Yong Do Cho, Insttute of Gas Safety R&D, Korea Gas Safety Corporaton, Shghung-Sh,

An Alternative Way to Measure Private Equity Performance

An Alternatve Way to Measure Prvate Equty Performance Peter Todd Parlux Investment Technology LLC Summary Internal Rate of Return (IRR) s probably the most common way to measure the performance of prvate

Feature selection for intrusion detection. Slobodan Petrović NISlab, Gjøvik University College

Feature selecton for ntruson detecton Slobodan Petrovć NISlab, Gjøvk Unversty College Contents The feature selecton problem Intruson detecton Traffc features relevant for IDS The CFS measure The mrmr measure

A COLLABORATIVE TRADING MODEL BY SUPPORT VECTOR REGRESSION AND TS FUZZY RULE FOR DAILY STOCK TURNING POINTS DETECTION

A COLLABORATIVE TRADING MODEL BY SUPPORT VECTOR REGRESSION AND TS FUZZY RULE FOR DAILY STOCK TURNING POINTS DETECTION JHENG-LONG WU, PEI-CHANN CHANG, KAI-TING CHANG Department of Informaton Management,

CHOLESTEROL REFERENCE METHOD LABORATORY NETWORK. Sample Stability Protocol

CHOLESTEROL REFERENCE METHOD LABORATORY NETWORK Sample Stablty Protocol Background The Cholesterol Reference Method Laboratory Network (CRMLN) developed certfcaton protocols for total cholesterol, HDL

The Short-term and Long-term Market

A Presentaton on Market Effcences to Northfeld Informaton Servces Annual Conference he Short-term and Long-term Market Effcences en Post Offce Square Boston, MA 0209 www.acadan-asset.com Charles H. Wang,

Can Auto Liability Insurance Purchases Signal Risk Attitude?

Internatonal Journal of Busness and Economcs, 2011, Vol. 10, No. 2, 159-164 Can Auto Lablty Insurance Purchases Sgnal Rsk Atttude? Chu-Shu L Department of Internatonal Busness, Asa Unversty, Tawan Sheng-Chang

A Genetic Programming Based Stock Price Predictor together with Mean-Variance Based Sell/Buy Actions

Proceedngs of the World Congress on Engneerng 28 Vol II WCE 28, July 2-4, 28, London, U.K. A Genetc Programmng Based Stock Prce Predctor together wth Mean-Varance Based Sell/Buy Actons Ramn Rajaboun and

Nonlinear data mapping by neural networks

Nonlnear data mappng by neural networks R.P.W. Dun Delft Unversty of Technology, Netherlands Abstract A revew s gven of the use of neural networks for nonlnear mappng of hgh dmensonal data on lower dmensonal

What is Candidate Sampling

What s Canddate Samplng Say we have a multclass or mult label problem where each tranng example ( x, T ) conssts of a context x a small (mult)set of target classes T out of a large unverse L of possble

A DATA MINING APPLICATION IN A STUDENT DATABASE

JOURNAL OF AERONAUTICS AND SPACE TECHNOLOGIES JULY 005 VOLUME NUMBER (53-57) A DATA MINING APPLICATION IN A STUDENT DATABASE Şenol Zafer ERDOĞAN Maltepe Ünversty Faculty of Engneerng Büyükbakkalköy-Istanbul

APPLICATION OF PROBE DATA COLLECTED VIA INFRARED BEACONS TO TRAFFIC MANEGEMENT

APPLICATION OF PROBE DATA COLLECTED VIA INFRARED BEACONS TO TRAFFIC MANEGEMENT Toshhko Oda (1), Kochro Iwaoka (2) (1), (2) Infrastructure Systems Busness Unt, Panasonc System Networks Co., Ltd. Saedo-cho

The Application of Fractional Brownian Motion in Option Pricing

Vol. 0, No. (05), pp. 73-8 http://dx.do.org/0.457/jmue.05.0..6 The Applcaton of Fractonal Brownan Moton n Opton Prcng Qng-xn Zhou School of Basc Scence,arbn Unversty of Commerce,arbn zhouqngxn98@6.com

THE efficient market hypothesis (EMH) asserts that financial. Predicting Financial Markets: Comparing Survey, News, Twitter and Search Engine Data

1 Predctng Fnancal Markets: Comparng Survey, News, Twtter and Search Engne Data Huna Mao, Indana Unversty-Bloomngton, Scott Counts, Mcrosoft Research, and Johan Bollen, Indana Unversty-Bloomngton arxv:1112.1051v1

A Secure Password-Authenticated Key Agreement Using Smart Cards

A Secure Password-Authentcated Key Agreement Usng Smart Cards Ka Chan 1, Wen-Chung Kuo 2 and Jn-Chou Cheng 3 1 Department of Computer and Informaton Scence, R.O.C. Mltary Academy, Kaohsung 83059, Tawan,

Quality Adjustment of Second-hand Motor Vehicle Application of Hedonic Approach in Hong Kong s Consumer Price Index

Qualty Adustment of Second-hand Motor Vehcle Applcaton of Hedonc Approach n Hong Kong s Consumer Prce Index Prepared for the 14 th Meetng of the Ottawa Group on Prce Indces 20 22 May 2015, Tokyo, Japan

Causal, Explanatory Forecasting. Analysis. Regression Analysis. Simple Linear Regression. Which is Independent? Forecasting

Causal, Explanatory Forecastng Assumes cause-and-effect relatonshp between system nputs and ts output Forecastng wth Regresson Analyss Rchard S. Barr Inputs System Cause + Effect Relatonshp The job of

Nasdaq Iceland Bond Indices 01 April 2015

Nasdaq Iceland Bond Indces 01 Aprl 2015 -Fxed duraton Indces Introducton Nasdaq Iceland (the Exchange) began calculatng ts current bond ndces n the begnnng of 2005. They were a response to recent changes

Credit Limit Optimization (CLO) for Credit Cards

Credt Lmt Optmzaton (CLO) for Credt Cards Vay S. Desa CSCC IX, Ednburgh September 8, 2005 Copyrght 2003, SAS Insttute Inc. All rghts reserved. SAS Propretary Agenda Background Tradtonal approaches to credt

1. Introduction. Graham Kendall School of Computer Science and IT ASAP Research Group University of Nottingham Nottingham, NG8 1BB gxk@cs.nott.ac.

The Co-evoluton of Tradng Strateges n A Mult-agent Based Smulated Stock Market Through the Integraton of Indvdual Learnng and Socal Learnng Graham Kendall School of Computer Scence and IT ASAP Research

Survey on Virtual Machine Placement Techniques in Cloud Computing Environment

Survey on Vrtual Machne Placement Technques n Cloud Computng Envronment Rajeev Kumar Gupta and R. K. Paterya Department of Computer Scence & Engneerng, MANIT, Bhopal, Inda ABSTRACT In tradtonal data center

Vision Mouse. Saurabh Sarkar a* University of Cincinnati, Cincinnati, USA ABSTRACT 1. INTRODUCTION

Vson Mouse Saurabh Sarkar a* a Unversty of Cncnnat, Cncnnat, USA ABSTRACT The report dscusses a vson based approach towards trackng of eyes and fngers. The report descrbes the process of locatng the possble

SPECIALIZED DAY TRADING - A NEW VIEW ON AN OLD GAME

August 7 - August 12, 2006 n Baden-Baden, Germany SPECIALIZED DAY TRADING - A NEW VIEW ON AN OLD GAME Vladmr Šmovć 1, and Vladmr Šmovć 2, PhD 1 Faculty of Electrcal Engneerng and Computng, Unska 3, 10000

Using Content-Based Filtering for Recommendation 1

Usng Content-Based Flterng for Recommendaton 1 Robn van Meteren 1 and Maarten van Someren 2 1 NetlnQ Group, Gerard Brandtstraat 26-28, 1054 JK, Amsterdam, The Netherlands, robn@netlnq.nl 2 Unversty of

MATHEMATICAL ENGINEERING TECHNICAL REPORTS. Sequential Optimizing Investing Strategy with Neural Networks

MATHEMATICAL ENGINEERING TECHNICAL REPORTS Sequental Optmzng Investng Strategy wth Neural Networks Ryo ADACHI and Akmch TAKEMURA METR 2010 03 February 2010 DEPARTMENT OF MATHEMATICAL INFORMATICS GRADUATE

Module 2 LOSSLESS IMAGE COMPRESSION SYSTEMS. Version 2 ECE IIT, Kharagpur

Module LOSSLESS IMAGE COMPRESSION SYSTEMS Lesson 3 Lossless Compresson: Huffman Codng Instructonal Objectves At the end of ths lesson, the students should be able to:. Defne and measure source entropy..

Multple-Perod Attrbuton: Resduals and Compoundng Our revewer gave these authors full marks for dealng wth an ssue that performance measurers and vendors often regard as propretary nformaton. In 1994, Dens

Traffic-light a stress test for life insurance provisions

MEMORANDUM Date 006-09-7 Authors Bengt von Bahr, Göran Ronge Traffc-lght a stress test for lfe nsurance provsons Fnansnspetonen P.O. Box 6750 SE-113 85 Stocholm [Sveavägen 167] Tel +46 8 787 80 00 Fax

Research Article Integrated Model of Multiple Kernel Learning and Differential Evolution for EUR/USD Trading

Hndaw Publshng Corporaton e Scentfc World Journal, Artcle ID 914641, 12 pages http://dx.do.org/10.1155/2014/914641 Research Artcle Integrated Model of Multple Kernel Learnng and Dfferental Evoluton for

THE APPLICATION OF DATA MINING TECHNIQUES AND MULTIPLE CLASSIFIERS TO MARKETING DECISION

Internatonal Journal of Electronc Busness Management, Vol. 3, No. 4, pp. 30-30 (2005) 30 THE APPLICATION OF DATA MINING TECHNIQUES AND MULTIPLE CLASSIFIERS TO MARKETING DECISION Yu-Mn Chang *, Yu-Cheh

Efficient Project Portfolio as a tool for Enterprise Risk Management

Effcent Proect Portfolo as a tool for Enterprse Rsk Management Valentn O. Nkonov Ural State Techncal Unversty Growth Traectory Consultng Company January 5, 27 Effcent Proect Portfolo as a tool for Enterprse

Study on Model of Risks Assessment of Standard Operation in Rural Power Network

Study on Model of Rsks Assessment of Standard Operaton n Rural Power Network Qngj L 1, Tao Yang 2 1 Qngj L, College of Informaton and Electrcal Engneerng, Shenyang Agrculture Unversty, Shenyang 110866,

An Interest-Oriented Network Evolution Mechanism for Online Communities

An Interest-Orented Network Evoluton Mechansm for Onlne Communtes Cahong Sun and Xaopng Yang School of Informaton, Renmn Unversty of Chna, Bejng 100872, P.R. Chna {chsun,yang}@ruc.edu.cn Abstract. Onlne

Sensitivity Analysis in a Generic Multi-Attribute Decision Support System

Senstvty Analyss n a Generc Mult-Attrbute Decson Support System Sxto Ríos-Insua, Antono Jménez and Alfonso Mateos Department of Artfcal Intellgence, Madrd Techncal Unversty Campus de Montegancedo s/n,

A hybrid global optimization algorithm based on parallel chaos optimization and outlook algorithm

Avalable onlne www.ocpr.com Journal of Chemcal and Pharmaceutcal Research, 2014, 6(7):1884-1889 Research Artcle ISSN : 0975-7384 CODEN(USA) : JCPRC5 A hybrd global optmzaton algorthm based on parallel

Simple Interest Loans (Section 5.1) :

Chapter 5 Fnance The frst part of ths revew wll explan the dfferent nterest and nvestment equatons you learned n secton 5.1 through 5.4 of your textbook and go through several examples. The second part

To manage leave, meeting institutional requirements and treating individual staff members fairly and consistently.

Corporate Polces & Procedures Human Resources - Document CPP216 Leave Management Frst Produced: Current Verson: Past Revsons: Revew Cycle: Apples From: 09/09/09 26/10/12 09/09/09 3 years Immedately Authorsaton:

BUSINESS PROCESS PERFORMANCE MANAGEMENT USING BAYESIAN BELIEF NETWORK. 0688, dskim@ssu.ac.kr

Proceedngs of the 41st Internatonal Conference on Computers & Industral Engneerng BUSINESS PROCESS PERFORMANCE MANAGEMENT USING BAYESIAN BELIEF NETWORK Yeong-bn Mn 1, Yongwoo Shn 2, Km Jeehong 1, Dongsoo

Risk-Oriented Decision Making During Integrated Investment Management under Uncertainty

Rsk-Orented Decson Makng Durng Integrated Investment Management under Uncertanty Valer Lovkn Abstract The model of decson-makng durng ntegrated nvestment management under uncertanty, whch enables to dstrbute

9.1 The Cumulative Sum Control Chart

Learnng Objectves 9.1 The Cumulatve Sum Control Chart 9.1.1 Basc Prncples: Cusum Control Chart for Montorng the Process Mean If s the target for the process mean, then the cumulatve sum control chart s

A Performance Analysis of View Maintenance Techniques for Data Warehouses

A Performance Analyss of Vew Mantenance Technques for Data Warehouses Xng Wang Dell Computer Corporaton Round Roc, Texas Le Gruenwald The nversty of Olahoma School of Computer Scence orman, OK 739 Guangtao

CHULMLEIGH ACADEMY TRUST INDUCTION AND DEVELOPMENT OF DIRECTORS POLICY

CHULMLEIGH ACADEMY TRUST INDUCTION AND DEVELOPMENT OF DIRECTORS POLICY Adopted by BoD:30 May 2012 Polcy Statement The Drectors of Chulmlegh Academy Trust beleve all Drectors brng an equally valued range

Scale Dependence of Overconfidence in Stock Market Volatility Forecasts

Scale Dependence of Overconfdence n Stoc Maret Volatlty Forecasts Marus Glaser, Thomas Langer, Jens Reynders, Martn Weber* June 7, 007 Abstract In ths study, we analyze whether volatlty forecasts (judgmental

Mining Feature Importance: Applying Evolutionary Algorithms within a Web-based Educational System

Mnng Feature Importance: Applyng Evolutonary Algorthms wthn a Web-based Educatonal System Behrouz MINAEI-BIDGOLI 1, and Gerd KORTEMEYER 2, and Wllam F. PUNCH 1 1 Genetc Algorthms Research and Applcatons

On the Optimal Control of a Cascade of Hydro-Electric Power Stations

On the Optmal Control of a Cascade of Hydro-Electrc Power Statons M.C.M. Guedes a, A.F. Rbero a, G.V. Smrnov b and S. Vlela c a Department of Mathematcs, School of Scences, Unversty of Porto, Portugal;

Using Series to Analyze Financial Situations: Present Value

2.8 Usng Seres to Analyze Fnancal Stuatons: Present Value In the prevous secton, you learned how to calculate the amount, or future value, of an ordnary smple annuty. The amount s the sum of the accumulated

Intelligent stock trading system by turning point confirming and probabilistic reasoning

Expert Systems wth Applcatons Expert Systems wth Applcatons 34 (2008) 620 627 www.elsever.com/locate/eswa Intellgent stock tradng system by turnng pont confrmng and probablstc reasonng Depe Bao *, Zehong

A Novel Auction Mechanism for Selling Time-Sensitive E-Services

A ovel Aucton Mechansm for Sellng Tme-Senstve E-Servces Juong-Sk Lee and Boleslaw K. Szymansk Optmaret Inc. and Department of Computer Scence Rensselaer Polytechnc Insttute 110 8 th Street, Troy, Y 12180,

Pricing Model of Cloud Computing Service with Partial Multihoming

Prcng Model of Cloud Computng Servce wth Partal Multhomng Zhang Ru 1 Tang Bng-yong 1 1.Glorous Sun School of Busness and Managment Donghua Unversty Shangha 251 Chna E-mal:ru528369@mal.dhu.edu.cn Abstract

MAPP. MERIS level 3 cloud and water vapour products. Issue: 1. Revision: 0. Date: 9.12.1998. Function Name Organisation Signature Date

Ttel: Project: Doc. No.: MERIS level 3 cloud and water vapour products MAPP MAPP-ATBD-ClWVL3 Issue: 1 Revson: 0 Date: 9.12.1998 Functon Name Organsaton Sgnature Date Author: Bennartz FUB Preusker FUB Schüller

Invoicing and Financial Forecasting of Time and Amount of Corresponding Cash Inflow

Dragan Smć Svetlana Smć Vasa Svrčevć Invocng and Fnancal Forecastng of Tme and Amount of Correspondng Cash Inflow Artcle Info:, Vol. 6 (2011), No. 3, pp. 014-021 Receved 13 Janyary 2011 Accepted 20 Aprl

Performance attribution for multi-layered investment decisions

Performance attrbuton for mult-layered nvestment decsons 880 Thrd Avenue 7th Floor Ne Yor, NY 10022 212.866.9200 t 212.866.9201 f qsnvestors.com Inna Oounova Head of Strategc Asset Allocaton Portfolo Management

PAS: A Packet Accounting System to Limit the Effects of DoS & DDoS. Debish Fesehaye & Klara Naherstedt University of Illinois-Urbana Champaign

PAS: A Packet Accountng System to Lmt the Effects of DoS & DDoS Debsh Fesehaye & Klara Naherstedt Unversty of Illnos-Urbana Champagn DoS and DDoS DDoS attacks are ncreasng threats to our dgtal world. Exstng

Bayesian Network Based Causal Relationship Identification and Funding Success Prediction in P2P Lending

Proceedngs of 2012 4th Internatonal Conference on Machne Learnng and Computng IPCSIT vol. 25 (2012) (2012) IACSIT Press, Sngapore Bayesan Network Based Causal Relatonshp Identfcaton and Fundng Success

Institute of Informatics, Faculty of Business and Management, Brno University of Technology,Czech Republic

Lagrange Multplers as Quanttatve Indcators n Economcs Ivan Mezník Insttute of Informatcs, Faculty of Busness and Management, Brno Unversty of TechnologCzech Republc Abstract The quanttatve role of Lagrange

Answer: A). There is a flatter IS curve in the high MPC economy. Original LM LM after increase in M. IS curve for low MPC economy

4.02 Quz Solutons Fall 2004 Multple-Choce Questons (30/00 ponts) Please, crcle the correct answer for each of the followng 0 multple-choce questons. For each queston, only one of the answers s correct.

Risk Model of Long-Term Production Scheduling in Open Pit Gold Mining

Rsk Model of Long-Term Producton Schedulng n Open Pt Gold Mnng R Halatchev 1 and P Lever 2 ABSTRACT Open pt gold mnng s an mportant sector of the Australan mnng ndustry. It uses large amounts of nvestments,

Improved SVM in Cloud Computing Information Mining

Internatonal Journal of Grd Dstrbuton Computng Vol.8, No.1 (015), pp.33-40 http://dx.do.org/10.1457/jgdc.015.8.1.04 Improved n Cloud Computng Informaton Mnng Lvshuhong (ZhengDe polytechnc college JangSu

Rank Based Clustering For Document Retrieval From Biomedical Databases

Jayanth Mancassamy et al /Internatonal Journal on Computer Scence and Engneerng Vol.1(2), 2009, 111-115 Rank Based Clusterng For Document Retreval From Bomedcal Databases Jayanth Mancassamy Department

Study on CET4 Marks in China s Graded English Teaching

Study on CET4 Marks n Chna s Graded Englsh Teachng CHE We College of Foregn Studes, Shandong Insttute of Busness and Technology, P.R.Chna, 264005 Abstract: Ths paper deploys Logt model, and decomposes

Overview. Naive Bayes Classifiers. A Sample Data Set. Frequencies and Probabilities. Connectionist and Statistical Language Processing

Overvew Nave Bayes Classfers Connectonst and Statstcal Language Processng Frank Keller keller@col.un-sb.de Computerlngustk Unverstät des Saarlandes Sample data set wth frequences and probabltes Classfcaton

Questions that we may have about the variables

Antono Olmos, 01 Multple Regresson Problem: we want to determne the effect of Desre for control, Famly support, Number of frends, and Score on the BDI test on Perceved Support of Latno women. Dependent

Planning for Marketing Campaigns

Plannng for Marketng Campagns Qang Yang and Hong Cheng Department of Computer Scence Hong Kong Unversty of Scence and Technology Clearwater Bay, Kowloon, Hong Kong, Chna (qyang, csch)@cs.ust.hk Abstract

INVESTIGATION OF VEHICULAR USERS FAIRNESS IN CDMA-HDR NETWORKS

21 22 September 2007, BULGARIA 119 Proceedngs of the Internatonal Conference on Informaton Technologes (InfoTech-2007) 21 st 22 nd September 2007, Bulgara vol. 2 INVESTIGATION OF VEHICULAR USERS FAIRNESS

On-Line Fault Detection in Wind Turbine Transmission System using Adaptive Filter and Robust Statistical Features

On-Lne Fault Detecton n Wnd Turbne Transmsson System usng Adaptve Flter and Robust Statstcal Features Ruoyu L Remote Dagnostcs Center SKF USA Inc. 3443 N. Sam Houston Pkwy., Houston TX 77086 Emal: ruoyu.l@skf.com

Project Networks With Mixed-Time Constraints

Project Networs Wth Mxed-Tme Constrants L Caccetta and B Wattananon Western Australan Centre of Excellence n Industral Optmsaton (WACEIO) Curtn Unversty of Technology GPO Box U1987 Perth Western Australa

Using Supervised Clustering Technique to Classify Received Messages in 137 Call Center of Tehran City Council

Usng Supervsed Clusterng Technque to Classfy Receved Messages n 137 Call Center of Tehran Cty Councl Mahdyeh Haghr 1*, Hamd Hassanpour 2 (1) Informaton Technology engneerng/e-commerce, Shraz Unversty (2)

Communication Networks II Contents

8 / 1 -- Communcaton Networs II (Görg) -- www.comnets.un-bremen.de Communcaton Networs II Contents 1 Fundamentals of probablty theory 2 Traffc n communcaton networs 3 Stochastc & Marovan Processes (SP

Learning with Imperfections A Multi-Agent Neural-Genetic Trading System. with Differing Levels of Social Learning

Proceedngs of the 4 IEEE Conference on Cybernetcs and Intellgent Systems Sngapore, 1-3 December, 4 Learnng wth Imperfectons A Mult-Agent Neural-Genetc Tradng System wth Dfferng Levels of Socal Learnng

Lecture 2: Single Layer Perceptrons Kevin Swingler

Lecture 2: Sngle Layer Perceptrons Kevn Sngler kms@cs.str.ac.uk Recap: McCulloch-Ptts Neuron Ths vastly smplfed model of real neurons s also knon as a Threshold Logc Unt: W 2 A Y 3 n W n. A set of synapses

7.5. Present Value of an Annuity. Investigate

7.5 Present Value of an Annuty Owen and Anna are approachng retrement and are puttng ther fnances n order. They have worked hard and nvested ther earnngs so that they now have a large amount of money on

JOURNAL OF INFORMATION SCIENCE AND ENGINEERING 28, 51-65 (2012) Semantc Lnk Analyss for Fndng Answer Experts * YAO LU 1,2,3, XIAOJUN QUAN 2, JINGSHENG LEI 4, XINGLIANG NI 1,2,3, WENYIN LIU 2,3 AND YINLONG

Search Efficient Representation of Healthcare Data based on the HL7 RIM

181 JOURNAL OF COMPUTERS, VOL. 5, NO. 12, DECEMBER 21 Search Effcent Representaton of Healthcare Data based on the HL7 RIM Razan Paul Department of Computer Scence and Engneerng, Bangladesh Unversty of

Indicators DZ and RDZ: essence, methods of calculation, signals and rules of trading

Investment Management and Fnancal Innovatons, Volume 8, Issue 3, 2011 Serhy Kozmenko (Ukrane), Oleksy Plastun (Ukrane) Indcators DZ and RDZ: essence, methods of calculaton, sgnals and rules of tradng Abstract

Calculation of Sampling Weights

Perre Foy Statstcs Canada 4 Calculaton of Samplng Weghts 4.1 OVERVIEW The basc sample desgn used n TIMSS Populatons 1 and 2 was a two-stage stratfed cluster desgn. 1 The frst stage conssted of a sample

Multivariate EWMA Control Chart

Multvarate EWMA Control Chart Summary The Multvarate EWMA Control Chart procedure creates control charts for two or more numerc varables. Examnng the varables n a multvarate sense s extremely mportant

Naive Rule Induction for Text Classification based on Key-phrases

Nave Rule Inducton for Text Classfcaton based on Key-phrases Nktas N. Karankolas & Chrstos Skourlas Department of Informatcs, Technologcal Educatonal Insttute of Athens, Greece. Abstract In ths paper,

AN APPOINTMENT ORDER OUTPATIENT SCHEDULING SYSTEM THAT IMPROVES OUTPATIENT EXPERIENCE

AN APPOINTMENT ORDER OUTPATIENT SCHEDULING SYSTEM THAT IMPROVES OUTPATIENT EXPERIENCE Yu-L Huang Industral Engneerng Department New Mexco State Unversty Las Cruces, New Mexco 88003, U.S.A. Abstract Patent

Construction Rules for Morningstar Canada Target Dividend Index SM

Constructon Rules for Mornngstar Canada Target Dvdend Index SM Mornngstar Methodology Paper October 2014 Verson 1.2 2014 Mornngstar, Inc. All rghts reserved. The nformaton n ths document s the property

Detecting Credit Card Fraud using Periodic Features

Detectng Credt Card Fraud usng Perodc Features Alejandro Correa Bahnsen, Djamla Aouada, Aleksandar Stojanovc and Björn Ottersten Interdscplnary Centre for Securty, Relablty and Trust Unversty of Luxembourg,

ANALYZING THE RELATIONSHIPS BETWEEN QUALITY, TIME, AND COST IN PROJECT MANAGEMENT DECISION MAKING

ANALYZING THE RELATIONSHIPS BETWEEN QUALITY, TIME, AND COST IN PROJECT MANAGEMENT DECISION MAKING Matthew J. Lberatore, Department of Management and Operatons, Vllanova Unversty, Vllanova, PA 19085, 610-519-4390,

Solution of Algebraic and Transcendental Equations

CHAPTER Soluton of Algerac and Transcendental Equatons. INTRODUCTION One of the most common prolem encountered n engneerng analyss s that gven a functon f (, fnd the values of for whch f ( = 0. The soluton

A Hybrid Model for Forecasting Sales in Turkish Paint Industry

Internatonal Journal of Computatonal Intellgence Systems, Vol.2, No. 3 (October, 2009), 277-287 A Hybrd Model for Forecastng Sales n Turksh Pant Industry Alp Ustundag * Department of Industral Engneerng,

Value-based Multiple Software Projects Scheduling with Genetic Algorithm Junchao Xiao, Qing Wang, Mingshu Li, Qiusong Yang, Lizi Xie, Dapeng Liu

Value-based Multple Software Projects Schedulng wth Genetc Algorthm Junchao Xao, Qng Wang, Mngshu L, Qusong Yang, Lz Xe, Dapeng Lu Laboratory for Internet Software Technologes Insttute of Software, Chnese

II. PROBABILITY OF AN EVENT

II. PROBABILITY OF AN EVENT As ndcated above, probablty s a quantfcaton, or a mathematcal model, of a random experment. Ths quantfcaton s a measure of the lkelhood that a gven event wll occur when the

On Mean Squared Error of Hierarchical Estimator

S C H E D A E I N F O R M A T I C A E VOLUME 0 0 On Mean Squared Error of Herarchcal Estmator Stans law Brodowsk Faculty of Physcs, Astronomy, and Appled Computer Scence, Jagellonan Unversty, Reymonta

Forecasting Spot Electricity Market Prices Using Time Series Models

C:\Documents and Settngs\Ethopa\Desktop\Forecastng Spot Electrcty Market Prces Usng Tme Seres Models.doc Forecastng Spot Electrcty Market Prces Usng Tme Seres Models by Dawt Halu Mazenga A thess Presented

The impact of hard discount control mechanism on the discount volatility of UK closed-end funds

Investment Management and Fnancal Innovatons, Volume 10, Issue 3, 2013 Ahmed F. Salhn (Egypt) The mpact of hard dscount control mechansm on the dscount volatlty of UK closed-end funds Abstract The mpact

RESEARCH ON DUAL-SHAKER SINE VIBRATION CONTROL. Yaoqi FENG 1, Hanping QIU 1. China Academy of Space Technology (CAST) yaoqi.feng@yahoo.

ICSV4 Carns Australa 9- July, 007 RESEARCH ON DUAL-SHAKER SINE VIBRATION CONTROL Yaoq FENG, Hanpng QIU Dynamc Test Laboratory, BISEE Chna Academy of Space Technology (CAST) yaoq.feng@yahoo.com Abstract

Online Appendix Supplemental Material for Market Microstructure Invariance: Empirical Hypotheses

Onlne Appendx Supplemental Materal for Market Mcrostructure Invarance: Emprcal Hypotheses Albert S. Kyle Unversty of Maryland akyle@rhsmth.umd.edu Anna A. Obzhaeva New Economc School aobzhaeva@nes.ru Table

Hybrid-Learning Methods for Stock Index Modeling

Hybrd-Learnng Methods for Stock Index Modelng 63 Chapter IV Hybrd-Learnng Methods for Stock Index Modelng Yuehu Chen, Jnan Unversty, Chna Ajth Abraham, Chung-Ang Unversty, Republc of Korea Abstract The

Mining Multiple Large Data Sources

The Internatonal Arab Journal of Informaton Technology, Vol. 7, No. 3, July 2 24 Mnng Multple Large Data Sources Anmesh Adhkar, Pralhad Ramachandrarao 2, Bhanu Prasad 3, and Jhml Adhkar 4 Department of

How Sets of Coherent Probabilities May Serve as Models for Degrees of Incoherence

1 st Internatonal Symposum on Imprecse Probabltes and Ther Applcatons, Ghent, Belgum, 29 June 2 July 1999 How Sets of Coherent Probabltes May Serve as Models for Degrees of Incoherence Mar J. Schervsh