Event driven trading new studies on innovative way. of trading in Forex market. Michał Osmoła INIME live 23 February 2016

Size: px
Start display at page:

Download "Event driven trading new studies on innovative way. of trading in Forex market. Michał Osmoła INIME live 23 February 2016"

Transcription

1 Event driven trading new studies on innovative way of trading in Forex market Michał Osmoła INIME live 23 February 2016

2 Forex market From Wikipedia: The foreign exchange market (Forex, FX, or currency market) is a global decentralized market for the trading of currencies. This includes all aspects of buying, selling and exchanging currencies at current or determined prices. Important Forex market features: Giant liquidity. Posibility of trading 24 hours a day, from 22:00 (UTC) on Sunday untill 22:00 (UTC) on Friday.

3 Macroeconomic announcements Macroeconomic indicators are information about the state and efficiency of a national economy. Features of macroeconomic announcements: Exact time of release is public information. Numeric format. Big news agency (like Bloomberg or Reuters) publish forecasts of the most meaningful indicators a few days before their release.

4 Event-driven trading

5 What cause such a reaction? Analysis confirm that the main factor which determine the direction of exchange rate movement is difference between actual and forecasted value of macroeconomic indicator. Behaviour of exchange rates right after macroeconomic indicator release is highly predictable for main macroeconomic news.

6 What is necessary to use this phenomenon in trading? Computer system able to get information about value of indicator immediately after announcement. Fast algorithm responsible for making a decision about taking a long/short position. Software which allow to place order instantly afrer release of indicator. Mechanism which monitor behaviour of exchange rate and decide about position close.

7 Modeling of EURUSD exchange rate First part of the research performed by INIME foundation checked the possibility of using ARIMA-GARCH and VEC models for make a short-term forecasts and apply them in closing-position system.

8 Unfortunately, performed analysis show that considered econometric models are too simple to explain complex nature of exchange rates. Forecasts obtain from ARIMA-GARCH and VEC models were useless in the context of optimal position closing. However, one interesting property of USDEUR, EURGBP and USDGBP exchange rates was discovered durning research. It was Granger casuality between given exchange rates after release US Nonfarm Payrolls indicator.

9 Granger Casuality test We say that a variable X that evolves over time Grangercauses another evolving variable Y if predictions of the value of Y based on its own past values and on the past values of X are better than predictions of Y based only on its own past values.

10 Application machine learning methods to Forex data Due to the fact that standard econometric methods are not useful in making short-term predictions, INIME foundation decided to try more sophisticated methods. We focus on two artificial intelligence concepts: Artifical Neural Networks Random Forests

11 Regression analysis Y dependent variable (real). X=(X1,...,Xn) independent variables (real). F(,β) some function from space X to space Y (model). β unknown parameters vector. O sample, i.e. set of pairs of observed values (x,y). E() - error function, defined on the sample O, describing the quality of assumptive model. Goal: Finding parameters vector β which minimize error function on sample O.

12 Artificial Neural Networks (ANN)

13 Neuron structure Source: https://upload.wikimedia.org/wikipedia/commons/thumb/6/60/artificialneuronmodel_english.png/600px-artificialneuronmodel_english.png Most common activation functions: Threshold function: Logistic function: Linear function: Hyperbolic tangent:

14 Scheme of feedforward artificial neural network

15 Data scaling ANN output often could be only the value from interval (0;1) or (-1,1) so we need to scale our data to proper format. It is a good manner to scale not only dependent variable but independent variables too. It often increase the speed of learning process and performance of ANN.

16 Backpropagation algorithm

17 Validation If we want to have reliable information about quality of obtained neural network, we need to perform some model test. Methods of validation: Simple dividing into learning set and test set (70/30). Dividing into learning set-validation set-test set (60/20/20). K-fold cross-validation.

18 Artificial Neural Networks advantages No need to know exact form of model. Could capture even very complicated structures of dependency between response and independent variables. Deal with noised data.

19 Random Forests Leo Breiman

20 Classification and regression tree (CART)

21 How to create CART? X set of independent variables in model Start at root node. Choose independent variable and split which minimize the sum of squared prediction errors over sample. Split sample O into two new subsets (new nodes). Check if stop rule is satisfied. If not search for split (across all leaf nodes which could be split) such that it maximize decrease of sum of squared errors over sample. The mean value of dependent variable over observation in choosen leaf node is prediction created by model for all cases which will get into that leaf.

22 How to build random forest? Set number of decision trees in forest. For each tree choose bootstrap sample from original sample O. Start create CART basing on given bootstrap sample. On each nodes split select at random k<<n independent variables which can be used to split operation. Create tree until maximum possible number of leaves will be reached (each observation will be in other leaf). Calculate mean of squared tree errors basing on observations which are not in bootstrap sample (OOB error). Repeat operation untill number of trees will be reached. The arithmetic mean of trees responses weighted by OOB error is prediction created by random forest for given observation.

23 Random forests advantages Do not overfit. One of the most effective machine learning techniques to deal with high-noise data. No need to know exact form of model function. Fast in calculation. Handles thousands of input variables without variable deletion. Gives estimates of what variables are important in the classification. OOB mean error is unbiased estimator of model error, no need to split data on test and learning set.

24 Using ANN and Random Forests in event-driven trading Goal: Predict maximum/minimum exchange rate value after release of macroeconomic indicator (on given time period).

25 Linear model

26

27 Neural Network Act.f=logistic

28

29 Random Forest Var.n=7

30

31 Summary Mean absolute error: Linear model: Neural network: Random Forest: Mean squared error: Linear model: Neural network: Random Forest:

32 Further studies Influence of volatility and spread on exchange rate dynamic. The end of trend detection. Finding patterns before and after macroeconomic releases. Durability of macroeconomic indicator influence. Creating of more sophisticated opening algorithms. Developing existed strategies and creating new ones.

33 Thank you for your attention Michał Osmoła T: E: Adress: 13A/1 Cystersów St., Cracow NIP: REGON: KRS:

Learning. Artificial Intelligence. Learning. Types of Learning. Inductive Learning Method. Inductive Learning. Learning.

Learning. Artificial Intelligence. Learning. Types of Learning. Inductive Learning Method. Inductive Learning. Learning. Learning Learning is essential for unknown environments, i.e., when designer lacks omniscience Artificial Intelligence Learning Chapter 8 Learning is useful as a system construction method, i.e., expose

More information

Decision Trees from large Databases: SLIQ

Decision Trees from large Databases: SLIQ Decision Trees from large Databases: SLIQ C4.5 often iterates over the training set How often? If the training set does not fit into main memory, swapping makes C4.5 unpractical! SLIQ: Sort the values

More information

Gerry Hobbs, Department of Statistics, West Virginia University

Gerry Hobbs, Department of Statistics, West Virginia University Decision Trees as a Predictive Modeling Method Gerry Hobbs, Department of Statistics, West Virginia University Abstract Predictive modeling has become an important area of interest in tasks such as credit

More information

Data Mining - Evaluation of Classifiers

Data Mining - Evaluation of Classifiers Data Mining - Evaluation of Classifiers Lecturer: JERZY STEFANOWSKI Institute of Computing Sciences Poznan University of Technology Poznan, Poland Lecture 4 SE Master Course 2008/2009 revised for 2010

More information

Lecture 10: Regression Trees

Lecture 10: Regression Trees Lecture 10: Regression Trees 36-350: Data Mining October 11, 2006 Reading: Textbook, sections 5.2 and 10.5. The next three lectures are going to be about a particular kind of nonlinear predictive model,

More information

Data Mining. Nonlinear Classification

Data Mining. Nonlinear Classification Data Mining Unit # 6 Sajjad Haider Fall 2014 1 Nonlinear Classification Classes may not be separable by a linear boundary Suppose we randomly generate a data set as follows: X has range between 0 to 15

More information

Data Mining Practical Machine Learning Tools and Techniques

Data Mining Practical Machine Learning Tools and Techniques Ensemble learning Data Mining Practical Machine Learning Tools and Techniques Slides for Chapter 8 of Data Mining by I. H. Witten, E. Frank and M. A. Hall Combining multiple models Bagging The basic idea

More information

An Introduction to Neural Networks

An Introduction to Neural Networks An Introduction to Vincent Cheung Kevin Cannons Signal & Data Compression Laboratory Electrical & Computer Engineering University of Manitoba Winnipeg, Manitoba, Canada Advisor: Dr. W. Kinsner May 27,

More information

Chapter 12 Discovering New Knowledge Data Mining

Chapter 12 Discovering New Knowledge Data Mining Chapter 12 Discovering New Knowledge Data Mining Becerra-Fernandez, et al. -- Knowledge Management 1/e -- 2004 Prentice Hall Additional material 2007 Dekai Wu Chapter Objectives Introduce the student to

More information

Lecture 6. Artificial Neural Networks

Lecture 6. Artificial Neural Networks Lecture 6 Artificial Neural Networks 1 1 Artificial Neural Networks In this note we provide an overview of the key concepts that have led to the emergence of Artificial Neural Networks as a major paradigm

More information

A practical guide to FX Arbitrage

A practical guide to FX Arbitrage A practical guide to FX Arbitrage FX Arbitrage is a highly debated topic in the FX community with many unknowns, as successful arbitrageurs may not be incentivized to disclose their methodology until after

More information

Artificial Neural Networks and Support Vector Machines. CS 486/686: Introduction to Artificial Intelligence

Artificial Neural Networks and Support Vector Machines. CS 486/686: Introduction to Artificial Intelligence Artificial Neural Networks and Support Vector Machines CS 486/686: Introduction to Artificial Intelligence 1 Outline What is a Neural Network? - Perceptron learners - Multi-layer networks What is a Support

More information

Cross Validation. Dr. Thomas Jensen Expedia.com

Cross Validation. Dr. Thomas Jensen Expedia.com Cross Validation Dr. Thomas Jensen Expedia.com About Me PhD from ETH Used to be a statistician at Link, now Senior Business Analyst at Expedia Manage a database with 720,000 Hotels that are not on contract

More information

Data Mining Part 5. Prediction

Data Mining Part 5. Prediction Data Mining Part 5. Prediction 5.7 Spring 2010 Instructor: Dr. Masoud Yaghini Outline Introduction Linear Regression Other Regression Models References Introduction Introduction Numerical prediction is

More information

Data Mining Algorithms Part 1. Dejan Sarka

Data Mining Algorithms Part 1. Dejan Sarka Data Mining Algorithms Part 1 Dejan Sarka Join the conversation on Twitter: @DevWeek #DW2015 Instructor Bio Dejan Sarka (dsarka@solidq.com) 30 years of experience SQL Server MVP, MCT, 13 books 7+ courses

More information

Fast Analytics on Big Data with H20

Fast Analytics on Big Data with H20 Fast Analytics on Big Data with H20 0xdata.com, h2o.ai Tomas Nykodym, Petr Maj Team About H2O and 0xdata H2O is a platform for distributed in memory predictive analytics and machine learning Pure Java,

More information

Data Mining Techniques Chapter 7: Artificial Neural Networks

Data Mining Techniques Chapter 7: Artificial Neural Networks Data Mining Techniques Chapter 7: Artificial Neural Networks Artificial Neural Networks.................................................. 2 Neural network example...................................................

More information

Generalizing Random Forests Principles to other Methods: Random MultiNomial Logit, Random Naive Bayes, Anita Prinzie & Dirk Van den Poel

Generalizing Random Forests Principles to other Methods: Random MultiNomial Logit, Random Naive Bayes, Anita Prinzie & Dirk Van den Poel Generalizing Random Forests Principles to other Methods: Random MultiNomial Logit, Random Naive Bayes, Anita Prinzie & Dirk Van den Poel Copyright 2008 All rights reserved. Random Forests Forest of decision

More information

Comparison of Data Mining Techniques used for Financial Data Analysis

Comparison of Data Mining Techniques used for Financial Data Analysis Comparison of Data Mining Techniques used for Financial Data Analysis Abhijit A. Sawant 1, P. M. Chawan 2 1 Student, 2 Associate Professor, Department of Computer Technology, VJTI, Mumbai, INDIA Abstract

More information

Neural Network Add-in

Neural Network Add-in Neural Network Add-in Version 1.5 Software User s Guide Contents Overview... 2 Getting Started... 2 Working with Datasets... 2 Open a Dataset... 3 Save a Dataset... 3 Data Pre-processing... 3 Lagging...

More information

8. Machine Learning Applied Artificial Intelligence

8. Machine Learning Applied Artificial Intelligence 8. Machine Learning Applied Artificial Intelligence Prof. Dr. Bernhard Humm Faculty of Computer Science Hochschule Darmstadt University of Applied Sciences 1 Retrospective Natural Language Processing Name

More information

6.2.8 Neural networks for data mining

6.2.8 Neural networks for data mining 6.2.8 Neural networks for data mining Walter Kosters 1 In many application areas neural networks are known to be valuable tools. This also holds for data mining. In this chapter we discuss the use of neural

More information

ARAM ZINZALIAN, DEYAN SIMEONOV, AND ELINA ROBEVA

ARAM ZINZALIAN, DEYAN SIMEONOV, AND ELINA ROBEVA FX FORECASTING WITH HYBRID SUPPORT VECTOR MACHINES ARAM ZINZALIAN, DEYAN SIMEONOV, AND ELINA ROBEVA 1 Introduction Foreign exchange rates are notoriously difficult to predict Deciding which of thousands

More information

Social Media Mining. Data Mining Essentials

Social Media Mining. Data Mining Essentials Introduction Data production rate has been increased dramatically (Big Data) and we are able store much more data than before E.g., purchase data, social media data, mobile phone data Businesses and customers

More information

Learning Example. Machine learning and our focus. Another Example. An example: data (loan application) The data and the goal

Learning Example. Machine learning and our focus. Another Example. An example: data (loan application) The data and the goal Learning Example Chapter 18: Learning from Examples 22c:145 An emergency room in a hospital measures 17 variables (e.g., blood pressure, age, etc) of newly admitted patients. A decision is needed: whether

More information

Advanced analytics at your hands

Advanced analytics at your hands 2.3 Advanced analytics at your hands Neural Designer is the most powerful predictive analytics software. It uses innovative neural networks techniques to provide data scientists with results in a way previously

More information

SINGULAR SPECTRUM ANALYSIS HYBRID FORECASTING METHODS WITH APPLICATION TO AIR TRANSPORT DEMAND

SINGULAR SPECTRUM ANALYSIS HYBRID FORECASTING METHODS WITH APPLICATION TO AIR TRANSPORT DEMAND SINGULAR SPECTRUM ANALYSIS HYBRID FORECASTING METHODS WITH APPLICATION TO AIR TRANSPORT DEMAND K. Adjenughwure, Delft University of Technology, Transport Institute, Ph.D. candidate V. Balopoulos, Democritus

More information

Highly Active Manual FX Trading Strategy. 1.Used indicators. 2. Theory. 2.1. Standard deviation (stddev Indicator - standard MetaTrader 4 Indicator)

Highly Active Manual FX Trading Strategy. 1.Used indicators. 2. Theory. 2.1. Standard deviation (stddev Indicator - standard MetaTrader 4 Indicator) Highly Active Manual FX Trading Strategy This strategy based on a mixture of two styles of trading: forex scalping, trend following short-term strategy. You can use it for any currency. Timeframe M15.

More information

Drug Store Sales Prediction

Drug Store Sales Prediction Drug Store Sales Prediction Chenghao Wang, Yang Li Abstract - In this paper we tried to apply machine learning algorithm into a real world problem drug store sales forecasting. Given store information,

More information

MS1b Statistical Data Mining

MS1b Statistical Data Mining MS1b Statistical Data Mining Yee Whye Teh Department of Statistics Oxford http://www.stats.ox.ac.uk/~teh/datamining.html Outline Administrivia and Introduction Course Structure Syllabus Introduction to

More information

The More Trees, the Better! Scaling Up Performance Using Random Forest in SAS Enterprise Miner

The More Trees, the Better! Scaling Up Performance Using Random Forest in SAS Enterprise Miner Paper 3361-2015 The More Trees, the Better! Scaling Up Performance Using Random Forest in SAS Enterprise Miner Narmada Deve Panneerselvam, Spears School of Business, Oklahoma State University, Stillwater,

More information

An Introduction to Advanced Analytics and Data Mining

An Introduction to Advanced Analytics and Data Mining An Introduction to Advanced Analytics and Data Mining Dr Barry Leventhal Henry Stewart Briefing on Marketing Analytics 19 th November 2010 Agenda What are Advanced Analytics and Data Mining? The toolkit

More information

Chapter 4: Artificial Neural Networks

Chapter 4: Artificial Neural Networks Chapter 4: Artificial Neural Networks CS 536: Machine Learning Littman (Wu, TA) Administration icml-03: instructional Conference on Machine Learning http://www.cs.rutgers.edu/~mlittman/courses/ml03/icml03/

More information

Neural Networks. CAP5610 Machine Learning Instructor: Guo-Jun Qi

Neural Networks. CAP5610 Machine Learning Instructor: Guo-Jun Qi Neural Networks CAP5610 Machine Learning Instructor: Guo-Jun Qi Recap: linear classifier Logistic regression Maximizing the posterior distribution of class Y conditional on the input vector X Support vector

More information

Chapter 6. The stacking ensemble approach

Chapter 6. The stacking ensemble approach 82 This chapter proposes the stacking ensemble approach for combining different data mining classifiers to get better performance. Other combination techniques like voting, bagging etc are also described

More information

An Introduction to Data Mining. Big Data World. Related Fields and Disciplines. What is Data Mining? 2/12/2015

An Introduction to Data Mining. Big Data World. Related Fields and Disciplines. What is Data Mining? 2/12/2015 An Introduction to Data Mining for Wind Power Management Spring 2015 Big Data World Every minute: Google receives over 4 million search queries Facebook users share almost 2.5 million pieces of content

More information

Ensemble Methods. Knowledge Discovery and Data Mining 2 (VU) (707.004) Roman Kern. KTI, TU Graz 2015-03-05

Ensemble Methods. Knowledge Discovery and Data Mining 2 (VU) (707.004) Roman Kern. KTI, TU Graz 2015-03-05 Ensemble Methods Knowledge Discovery and Data Mining 2 (VU) (707004) Roman Kern KTI, TU Graz 2015-03-05 Roman Kern (KTI, TU Graz) Ensemble Methods 2015-03-05 1 / 38 Outline 1 Introduction 2 Classification

More information

The relation between news events and stock price jump: an analysis based on neural network

The relation between news events and stock price jump: an analysis based on neural network 20th International Congress on Modelling and Simulation, Adelaide, Australia, 1 6 December 2013 www.mssanz.org.au/modsim2013 The relation between news events and stock price jump: an analysis based on

More information

Knowledge Discovery and Data Mining. Bootstrap review. Bagging Important Concepts. Notes. Lecture 19 - Bagging. Tom Kelsey. Notes

Knowledge Discovery and Data Mining. Bootstrap review. Bagging Important Concepts. Notes. Lecture 19 - Bagging. Tom Kelsey. Notes Knowledge Discovery and Data Mining Lecture 19 - Bagging Tom Kelsey School of Computer Science University of St Andrews http://tom.host.cs.st-andrews.ac.uk twk@st-andrews.ac.uk Tom Kelsey ID5059-19-B &

More information

The multilayer sentiment analysis model based on Random forest Wei Liu1, Jie Zhang2

The multilayer sentiment analysis model based on Random forest Wei Liu1, Jie Zhang2 2nd International Conference on Advances in Mechanical Engineering and Industrial Informatics (AMEII 2016) The multilayer sentiment analysis model based on Random forest Wei Liu1, Jie Zhang2 1 School of

More information

Numerical Algorithms Group

Numerical Algorithms Group Title: Summary: Using the Component Approach to Craft Customized Data Mining Solutions One definition of data mining is the non-trivial extraction of implicit, previously unknown and potentially useful

More information

Searching for Gravitational Waves from the Coalescence of High Mass Black Hole Binaries

Searching for Gravitational Waves from the Coalescence of High Mass Black Hole Binaries Searching for Gravitational Waves from the Coalescence of High Mass Black Hole Binaries 2015 SURE Presentation September 22 nd, 2015 Lau Ka Tung Department of Physics, The Chinese University of Hong Kong

More information

The Artificial Prediction Market

The Artificial Prediction Market The Artificial Prediction Market Adrian Barbu Department of Statistics Florida State University Joint work with Nathan Lay, Siemens Corporate Research 1 Overview Main Contributions A mathematical theory

More information

Classification and Regression Trees

Classification and Regression Trees Classification and Regression Trees Bob Stine Dept of Statistics, School University of Pennsylvania Trees Familiar metaphor Biology Decision tree Medical diagnosis Org chart Properties Recursive, partitioning

More information

International Journal of Computer Science Trends and Technology (IJCST) Volume 2 Issue 3, May-Jun 2014

International Journal of Computer Science Trends and Technology (IJCST) Volume 2 Issue 3, May-Jun 2014 RESEARCH ARTICLE OPEN ACCESS A Survey of Data Mining: Concepts with Applications and its Future Scope Dr. Zubair Khan 1, Ashish Kumar 2, Sunny Kumar 3 M.Tech Research Scholar 2. Department of Computer

More information

Using multiple models: Bagging, Boosting, Ensembles, Forests

Using multiple models: Bagging, Boosting, Ensembles, Forests Using multiple models: Bagging, Boosting, Ensembles, Forests Bagging Combining predictions from multiple models Different models obtained from bootstrap samples of training data Average predictions or

More information

Three types of messages: A, B, C. Assume A is the oldest type, and C is the most recent type.

Three types of messages: A, B, C. Assume A is the oldest type, and C is the most recent type. Chronological Sampling for Email Filtering Ching-Lung Fu 2, Daniel Silver 1, and James Blustein 2 1 Acadia University, Wolfville, Nova Scotia, Canada 2 Dalhousie University, Halifax, Nova Scotia, Canada

More information

Neural Networks and Support Vector Machines

Neural Networks and Support Vector Machines INF5390 - Kunstig intelligens Neural Networks and Support Vector Machines Roar Fjellheim INF5390-13 Neural Networks and SVM 1 Outline Neural networks Perceptrons Neural networks Support vector machines

More information

Applied Multivariate Analysis - Big data analytics

Applied Multivariate Analysis - Big data analytics Applied Multivariate Analysis - Big data analytics Nathalie Villa-Vialaneix nathalie.villa@toulouse.inra.fr http://www.nathalievilla.org M1 in Economics and Economics and Statistics Toulouse School of

More information

INTELLIGENT ENERGY MANAGEMENT OF ELECTRICAL POWER SYSTEMS WITH DISTRIBUTED FEEDING ON THE BASIS OF FORECASTS OF DEMAND AND GENERATION Chr.

INTELLIGENT ENERGY MANAGEMENT OF ELECTRICAL POWER SYSTEMS WITH DISTRIBUTED FEEDING ON THE BASIS OF FORECASTS OF DEMAND AND GENERATION Chr. INTELLIGENT ENERGY MANAGEMENT OF ELECTRICAL POWER SYSTEMS WITH DISTRIBUTED FEEDING ON THE BASIS OF FORECASTS OF DEMAND AND GENERATION Chr. Meisenbach M. Hable G. Winkler P. Meier Technology, Laboratory

More information

Variable selection using random forests

Variable selection using random forests Pattern Recognition Letters 31 (2010) January 25, 2012 Outline 1 2 Sensitivity to n and p Sensitivity to mtry and ntree 3 Procedure Starting example 4 Prostate data Four high dimensional classication datasets

More information

Smart Grid Data Analytics for Decision Support

Smart Grid Data Analytics for Decision Support 1 Smart Grid Data Analytics for Decision Support Prakash Ranganathan, Department of Electrical Engineering, University of North Dakota, Grand Forks, ND, USA Prakash.Ranganathan@engr.und.edu, 701-777-4431

More information

Data Mining Classification: Decision Trees

Data Mining Classification: Decision Trees Data Mining Classification: Decision Trees Classification Decision Trees: what they are and how they work Hunt s (TDIDT) algorithm How to select the best split How to handle Inconsistent data Continuous

More information

Model Combination. 24 Novembre 2009

Model Combination. 24 Novembre 2009 Model Combination 24 Novembre 2009 Datamining 1 2009-2010 Plan 1 Principles of model combination 2 Resampling methods Bagging Random Forests Boosting 3 Hybrid methods Stacking Generic algorithm for mulistrategy

More information

The Combination Forecasting Model of Auto Sales Based on Seasonal Index and RBF Neural Network

The Combination Forecasting Model of Auto Sales Based on Seasonal Index and RBF Neural Network , pp.67-76 http://dx.doi.org/10.14257/ijdta.2016.9.1.06 The Combination Forecasting Model of Auto Sales Based on Seasonal Index and RBF Neural Network Lihua Yang and Baolin Li* School of Economics and

More information

CHAPTER 6 NEURAL NETWORK BASED SURFACE ROUGHNESS ESTIMATION

CHAPTER 6 NEURAL NETWORK BASED SURFACE ROUGHNESS ESTIMATION CHAPTER 6 NEURAL NETWORK BASED SURFACE ROUGHNESS ESTIMATION 6.1. KNOWLEDGE REPRESENTATION The function of any representation scheme is to capture the essential features of a problem domain and make that

More information

Classification of Bad Accounts in Credit Card Industry

Classification of Bad Accounts in Credit Card Industry Classification of Bad Accounts in Credit Card Industry Chengwei Yuan December 12, 2014 Introduction Risk management is critical for a credit card company to survive in such competing industry. In addition

More information

NEURAL NETWORKS IN DATA MINING

NEURAL NETWORKS IN DATA MINING NEURAL NETWORKS IN DATA MINING 1 DR. YASHPAL SINGH, 2 ALOK SINGH CHAUHAN 1 Reader, Bundelkhand Institute of Engineering & Technology, Jhansi, India 2 Lecturer, United Institute of Management, Allahabad,

More information

Fine Particulate Matter Concentration Level Prediction by using Tree-based Ensemble Classification Algorithms

Fine Particulate Matter Concentration Level Prediction by using Tree-based Ensemble Classification Algorithms Fine Particulate Matter Concentration Level Prediction by using Tree-based Ensemble Classification Algorithms Yin Zhao School of Mathematical Sciences Universiti Sains Malaysia (USM) Penang, Malaysia Yahya

More information

Data mining and statistical models in marketing campaigns of BT Retail

Data mining and statistical models in marketing campaigns of BT Retail Data mining and statistical models in marketing campaigns of BT Retail Francesco Vivarelli and Martyn Johnson Database Exploitation, Segmentation and Targeting group BT Retail Pp501 Holborn centre 120

More information

An Overview of Data Mining: Predictive Modeling for IR in the 21 st Century

An Overview of Data Mining: Predictive Modeling for IR in the 21 st Century An Overview of Data Mining: Predictive Modeling for IR in the 21 st Century Nora Galambos, PhD Senior Data Scientist Office of Institutional Research, Planning & Effectiveness Stony Brook University AIRPO

More information

Financial Econometrics and Volatility Models Introduction to High Frequency Data

Financial Econometrics and Volatility Models Introduction to High Frequency Data Financial Econometrics and Volatility Models Introduction to High Frequency Data Eric Zivot May 17, 2010 Lecture Outline Introduction and Motivation High Frequency Data Sources Challenges to Statistical

More information

Prediction Model for Crude Oil Price Using Artificial Neural Networks

Prediction Model for Crude Oil Price Using Artificial Neural Networks Applied Mathematical Sciences, Vol. 8, 2014, no. 80, 3953-3965 HIKARI Ltd, www.m-hikari.com http://dx.doi.org/10.12988/ams.2014.43193 Prediction Model for Crude Oil Price Using Artificial Neural Networks

More information

Beating the MLB Moneyline

Beating the MLB Moneyline Beating the MLB Moneyline Leland Chen llxchen@stanford.edu Andrew He andu@stanford.edu 1 Abstract Sports forecasting is a challenging task that has similarities to stock market prediction, requiring time-series

More information

A Property & Casualty Insurance Predictive Modeling Process in SAS

A Property & Casualty Insurance Predictive Modeling Process in SAS Paper AA-02-2015 A Property & Casualty Insurance Predictive Modeling Process in SAS 1.0 ABSTRACT Mei Najim, Sedgwick Claim Management Services, Chicago, Illinois Predictive analytics has been developing

More information

Improving Generalization

Improving Generalization Improving Generalization Introduction to Neural Networks : Lecture 10 John A. Bullinaria, 2004 1. Improving Generalization 2. Training, Validation and Testing Data Sets 3. Cross-Validation 4. Weight Restriction

More information

Trees and Random Forests

Trees and Random Forests Trees and Random Forests Adele Cutler Professor, Mathematics and Statistics Utah State University This research is partially supported by NIH 1R15AG037392-01 Cache Valley, Utah Utah State University Leo

More information

FORECASTING THE JORDANIAN STOCK PRICES USING ARTIFICIAL NEURAL NETWORK

FORECASTING THE JORDANIAN STOCK PRICES USING ARTIFICIAL NEURAL NETWORK 1 FORECASTING THE JORDANIAN STOCK PRICES USING ARTIFICIAL NEURAL NETWORK AYMAN A. ABU HAMMAD Civil Engineering Department Applied Science University P. O. Box: 926296, Amman 11931 Jordan SOUMA M. ALHAJ

More information

Weather forecast prediction: a Data Mining application

Weather forecast prediction: a Data Mining application Weather forecast prediction: a Data Mining application Ms. Ashwini Mandale, Mrs. Jadhawar B.A. Assistant professor, Dr.Daulatrao Aher College of engg,karad,ashwini.mandale@gmail.com,8407974457 Abstract

More information

Hedging Illiquid FX Options: An Empirical Analysis of Alternative Hedging Strategies

Hedging Illiquid FX Options: An Empirical Analysis of Alternative Hedging Strategies Hedging Illiquid FX Options: An Empirical Analysis of Alternative Hedging Strategies Drazen Pesjak Supervised by A.A. Tsvetkov 1, D. Posthuma 2 and S.A. Borovkova 3 MSc. Thesis Finance HONOURS TRACK Quantitative

More information

Data Mining Methods: Applications for Institutional Research

Data Mining Methods: Applications for Institutional Research Data Mining Methods: Applications for Institutional Research Nora Galambos, PhD Office of Institutional Research, Planning & Effectiveness Stony Brook University NEAIR Annual Conference Philadelphia 2014

More information

Neural Networks for Sentiment Detection in Financial Text

Neural Networks for Sentiment Detection in Financial Text Neural Networks for Sentiment Detection in Financial Text Caslav Bozic* and Detlef Seese* With a rise of algorithmic trading volume in recent years, the need for automatic analysis of financial news emerged.

More information

Hong Kong Stock Index Forecasting

Hong Kong Stock Index Forecasting Hong Kong Stock Index Forecasting Tong Fu Shuo Chen Chuanqi Wei tfu1@stanford.edu cslcb@stanford.edu chuanqi@stanford.edu Abstract Prediction of the movement of stock market is a long-time attractive topic

More information

TIME SERIES FORECASTING WITH NEURAL NETWORK: A CASE STUDY OF STOCK PRICES OF INTERCONTINENTAL BANK NIGERIA

TIME SERIES FORECASTING WITH NEURAL NETWORK: A CASE STUDY OF STOCK PRICES OF INTERCONTINENTAL BANK NIGERIA www.arpapress.com/volumes/vol9issue3/ijrras_9_3_16.pdf TIME SERIES FORECASTING WITH NEURAL NETWORK: A CASE STUDY OF STOCK PRICES OF INTERCONTINENTAL BANK NIGERIA 1 Akintola K.G., 2 Alese B.K. & 2 Thompson

More information

Neural Network Applications in Stock Market Predictions - A Methodology Analysis

Neural Network Applications in Stock Market Predictions - A Methodology Analysis Neural Network Applications in Stock Market Predictions - A Methodology Analysis Marijana Zekic, MS University of Josip Juraj Strossmayer in Osijek Faculty of Economics Osijek Gajev trg 7, 31000 Osijek

More information

Prediction of Stock Performance Using Analytical Techniques

Prediction of Stock Performance Using Analytical Techniques 136 JOURNAL OF EMERGING TECHNOLOGIES IN WEB INTELLIGENCE, VOL. 5, NO. 2, MAY 2013 Prediction of Stock Performance Using Analytical Techniques Carol Hargreaves Institute of Systems Science National University

More information

Forecasting Hospital Bed Availability Using Simulation and Neural Networks

Forecasting Hospital Bed Availability Using Simulation and Neural Networks Forecasting Hospital Bed Availability Using Simulation and Neural Networks Matthew J. Daniels Michael E. Kuhl Industrial & Systems Engineering Department Rochester Institute of Technology Rochester, NY

More information

Data quality in Accounting Information Systems

Data quality in Accounting Information Systems Data quality in Accounting Information Systems Comparing Several Data Mining Techniques Erjon Zoto Department of Statistics and Applied Informatics Faculty of Economy, University of Tirana Tirana, Albania

More information

Pentaho Data Mining Last Modified on January 22, 2007

Pentaho Data Mining Last Modified on January 22, 2007 Pentaho Data Mining Copyright 2007 Pentaho Corporation. Redistribution permitted. All trademarks are the property of their respective owners. For the latest information, please visit our web site at www.pentaho.org

More information

The Impact of Big Data on Classic Machine Learning Algorithms. Thomas Jensen, Senior Business Analyst @ Expedia

The Impact of Big Data on Classic Machine Learning Algorithms. Thomas Jensen, Senior Business Analyst @ Expedia The Impact of Big Data on Classic Machine Learning Algorithms Thomas Jensen, Senior Business Analyst @ Expedia Who am I? Senior Business Analyst @ Expedia Working within the competitive intelligence unit

More information

Knowledge Discovery and Data Mining

Knowledge Discovery and Data Mining Knowledge Discovery and Data Mining Unit # 6 Sajjad Haider Fall 2014 1 Evaluating the Accuracy of a Classifier Holdout, random subsampling, crossvalidation, and the bootstrap are common techniques for

More information

Introduction to Machine Learning. Speaker: Harry Chao Advisor: J.J. Ding Date: 1/27/2011

Introduction to Machine Learning. Speaker: Harry Chao Advisor: J.J. Ding Date: 1/27/2011 Introduction to Machine Learning Speaker: Harry Chao Advisor: J.J. Ding Date: 1/27/2011 1 Outline 1. What is machine learning? 2. The basic of machine learning 3. Principles and effects of machine learning

More information

Neural Networks and Back Propagation Algorithm

Neural Networks and Back Propagation Algorithm Neural Networks and Back Propagation Algorithm Mirza Cilimkovic Institute of Technology Blanchardstown Blanchardstown Road North Dublin 15 Ireland mirzac@gmail.com Abstract Neural Networks (NN) are important

More information

Neural Networks & Boosting

Neural Networks & Boosting Neural Networks & Boosting Bob Stine Dept of Statistics, School University of Pennsylvania Questions How is logistic regression different from OLS? Logistic mean function for probabilities Larger weight

More information

Machine learning for algo trading

Machine learning for algo trading Machine learning for algo trading An introduction for nonmathematicians Dr. Aly Kassam Overview High level introduction to machine learning A machine learning bestiary What has all this got to do with

More information

Car Insurance. Havránek, Pokorný, Tomášek

Car Insurance. Havránek, Pokorný, Tomášek Car Insurance Havránek, Pokorný, Tomášek Outline Data overview Horizontal approach + Decision tree/forests Vertical (column) approach + Neural networks SVM Data overview Customers Viewed policies Bought

More information

Neural Networks. Neural network is a network or circuit of neurons. Neurons can be. Biological neurons Artificial neurons

Neural Networks. Neural network is a network or circuit of neurons. Neurons can be. Biological neurons Artificial neurons Neural Networks Neural network is a network or circuit of neurons Neurons can be Biological neurons Artificial neurons Biological neurons Building block of the brain Human brain contains over 10 billion

More information

Statistics for BIG data

Statistics for BIG data Statistics for BIG data Statistics for Big Data: Are Statisticians Ready? Dennis Lin Department of Statistics The Pennsylvania State University John Jordan and Dennis K.J. Lin (ICSA-Bulletine 2014) Before

More information

INTRODUCTION TO NEURAL NETWORKS

INTRODUCTION TO NEURAL NETWORKS INTRODUCTION TO NEURAL NETWORKS Pictures are taken from http://www.cs.cmu.edu/~tom/mlbook-chapter-slides.html http://research.microsoft.com/~cmbishop/prml/index.htm By Nobel Khandaker Neural Networks An

More information

Predicting borrowers chance of defaulting on credit loans

Predicting borrowers chance of defaulting on credit loans Predicting borrowers chance of defaulting on credit loans Junjie Liang (junjie87@stanford.edu) Abstract Credit score prediction is of great interests to banks as the outcome of the prediction algorithm

More information

Drugs store sales forecast using Machine Learning

Drugs store sales forecast using Machine Learning Drugs store sales forecast using Machine Learning Hongyu Xiong (hxiong2), Xi Wu (wuxi), Jingying Yue (jingying) 1 Introduction Nowadays medical-related sales prediction is of great interest; with reliable

More information

Forex Trading Strategies: One way to trade the Non Farm Payroll report.

Forex Trading Strategies: One way to trade the Non Farm Payroll report. Forex Trading Strategies: One way to trade the Non Farm Payroll report. June 3 2011 1 Upshot Trade Signals disclaimer The information provided in this report is for educational purposes only. It is not

More information

Predictive modelling around the world 28.11.13

Predictive modelling around the world 28.11.13 Predictive modelling around the world 28.11.13 Agenda Why this presentation is really interesting Introduction to predictive modelling Case studies Conclusions Why this presentation is really interesting

More information

Forecasting Trade Direction and Size of Future Contracts Using Deep Belief Network

Forecasting Trade Direction and Size of Future Contracts Using Deep Belief Network Forecasting Trade Direction and Size of Future Contracts Using Deep Belief Network Anthony Lai (aslai), MK Li (lilemon), Foon Wang Pong (ppong) Abstract Algorithmic trading, high frequency trading (HFT)

More information

OPTIMIZATION AND FORECASTING WITH FINANCIAL TIME SERIES

OPTIMIZATION AND FORECASTING WITH FINANCIAL TIME SERIES OPTIMIZATION AND FORECASTING WITH FINANCIAL TIME SERIES Allan Din Geneva Research Collaboration Notes from seminar at CERN, June 25, 2002 General scope of GRC research activities Econophysics paradigm

More information

Knowledge Discovery and Data Mining

Knowledge Discovery and Data Mining Knowledge Discovery and Data Mining Unit # 11 Sajjad Haider Fall 2013 1 Supervised Learning Process Data Collection/Preparation Data Cleaning Discretization Supervised/Unuspervised Identification of right

More information

International Journal of Electronics and Computer Science Engineering 1449

International Journal of Electronics and Computer Science Engineering 1449 International Journal of Electronics and Computer Science Engineering 1449 Available Online at www.ijecse.org ISSN- 2277-1956 Neural Networks in Data Mining Priyanka Gaur Department of Information and

More information

Insurance Analytics - analýza dat a prediktivní modelování v pojišťovnictví. Pavel Kříž. Seminář z aktuárských věd MFF 4.

Insurance Analytics - analýza dat a prediktivní modelování v pojišťovnictví. Pavel Kříž. Seminář z aktuárských věd MFF 4. Insurance Analytics - analýza dat a prediktivní modelování v pojišťovnictví Pavel Kříž Seminář z aktuárských věd MFF 4. dubna 2014 Summary 1. Application areas of Insurance Analytics 2. Insurance Analytics

More information

Government of Russian Federation. Faculty of Computer Science School of Data Analysis and Artificial Intelligence

Government of Russian Federation. Faculty of Computer Science School of Data Analysis and Artificial Intelligence Government of Russian Federation Federal State Autonomous Educational Institution of High Professional Education National Research University «Higher School of Economics» Faculty of Computer Science School

More information

International Journal of Computer Trends and Technology (IJCTT) volume 4 Issue 8 August 2013

International Journal of Computer Trends and Technology (IJCTT) volume 4 Issue 8 August 2013 A Short-Term Traffic Prediction On A Distributed Network Using Multiple Regression Equation Ms.Sharmi.S 1 Research Scholar, MS University,Thirunelvelli Dr.M.Punithavalli Director, SREC,Coimbatore. Abstract:

More information