## Transforming numeric data into relations

Two time series TR (training set) and CT (control test set) of the target variable are used to train and evaluate a forecasting algorithm, where is ten years of data (1985-1994, tr 2528 trading days) and is two years of data (1995-1996, ct - 506 trading days). Five sequential days are used as the main forecast unit (an object) where a*, is j-th day of the five-day object at. We also use another notation with the correspondence between notations a(i-i)*j> at for all five days of a,...

## There is a theorem [Vityaev 1992 that all rules which have a maximum value of conditional probability can be found at

The algorithm stops generating new rules when they become too complex (i.e., statistically insignificant for the data) even if the rules are highly accurate on training data. The Fisher statistical test is used in this algorithm for testing statistical significance. The obvious other stop criterion is the time limitation. Below MMDR is presented more formally using standard first-order logic terms described in Section 4.4.3. Let us consider the set RL of all possible rules of the form where...

## Neural Networks

Neural networks are widely presented in many available publications, therefore in this section we present only a short overview of neural networks based on terms and notation from Russel, Norvig, 1995 and Mitchell, 1997 . Section 2.5 is devoted to a new approach for testing neural networks and other data mining methods. Following that, section 2.6 discusses financial applications of neural networks. A neural network can be viewed as consisting of four components where U is a set of units...

## MMDR pseudocode

MMDR pseudocode is presented is this Section in C style as a value returning function MMDR D, InitialRuleSet with two parameters data D and set of initial rules InitialRuleSet . This function returns a learned rule Learned_rule as a set of Horn clauses. Table 4.22. MM PR function returning a learned rule _ MM DR Data Type D, RuleTypeSet InitialRuleSet i 1 set up the first level depth of search tree to find a rule NewRule i Some_rule_from InitialRuleSet If Is_Regu larity NewRu le i ,D...

## Gregory Piatetsky Shapiro Boston Massachusetts

The new generation of computing techniques collectively called data mining methods are now applied to stock market analysis, predictions, and other financial applications. In this book we discuss the relative merits of these methods for financial modeling and present a comprehensive survey of current capabilities of these methods in financial analysis. The focus is on the specific and highly topical issue of adaptive linear and non-linear mining of financial data. Topics are progressively...

## Extracting decision trees from neural networks

The rule-based symbolic approach described in Sections 3.1 and 3.2 represents learned solutions in an understandable form. Therefore, end users can examine the learned solutions independent of their understanding of the underlying mathematical tools. This type of representation is difficult to obtain for neural networks. Neural network representations are usually incomprehensible to humans Shavlik, 1994 Graven, Shavlik, 1997 . However, the pure rule-based approach ignores valuable findings...

## Numerical relational data mining

Rapid growth of databases is accompanied by growing variety of types of these data. Relational data mining has unique capabilities for discovering a wide range of human-readable, understandable regularities in databases with various data types. However, the use of symbolic relational data for numerical forecast and discovery regularities in numerical data requires the solution of two problems. Problem 1 is the development of a mechanism for transformation between numerical and relational data...

## C 05tyyd205kxxd2

The first term, 0.5 y-yd 2 gt measures the cost of learning by weighting the squared deviation between the network's output y and the target value yd. The second term, 0.5k x-xd J, measures the cost of cleaning by squaring the deviation between the cleaned input x and the actual input xd. The parameters ti and k are the learning rate and the cleaning rate, respectively. The cleaning technique assumes that both inputs and outputs are corrupted by noise. Correcting the inputs and outputs can...

## Examples

In this section, several examples illustrate the difference between relational and attribute-value languages used in Data Mining. In an attribute-value language, objects are described by tuples of attribute-value pairs, where each attribute represents some characteristic of the object, e.g., share price, volume, etc. Neural networks and many other attribute-value learning systems have been used in financial forecasting for years see chapter 3 . Alternative attribute-value learning systems...

## Both a trained network and training data

In the second and third cases, a neural network serves as an oracle, which can produce target values for many thousands of artificial training examples. If the oracle is well trained then these additional examples can help significantly in designing a decision tree. This means that there is no significant limitation on the amount of available artificial training data for training a decision tree. The Trepan algorithm exemplifies extraction of a decision tree from a trained neural network. The...

## Multipleattribute splitting test used in an enhanced version of ID2of3 and Trepan algorithms [Murphy Pazzani 1991

Figure 3.5 illustrates the idea of single-attribute splitting tests like xj 17.71. Another example of a single-attribute test is 5 lt xj lt 10. The multiple-attribute splitting test in ID2-of-3 uses m-of-n expressions for tests at its internal nodes. The multiple-attribute splitting test can discover rules such Rule MFR IF x, l amp x2 l OR x3 l amp X4 l OR x, l amp x3 l OR x, l amp X4 l OR x2 l amp x3 l OR x2 l amp x, l This rule is a 2-of-4 type rule, i.e., the rule is true if any two of four...

## Hidden Markov models in finance

A model for predicting the daily probability distribution of SP500 returns is developed in Weigend, Shi, 1997, 1998 . The goal of predicting a probability distribution is significantly different from the typical goal in finance -- predicting the next value of the time series. The probability distribution delivers a wider picture of the possible future of the stock market. The full probability density function predictions are composed by a weighted superposition of the individual densities....

## Data mining definitions and practice

Two learning approaches are used in data mining 1. supervised pattern learning -- learning with known classes for training examples and 2. unsupervised pattern learning -- learning without known classes for training examples. This book is focused on the supervised learning. The common attribute- based representation of a supervised learning includes Zighed, 1996 - W w , a sample, called the training sample, chosen from a population. Each individual w in W is called a training example. - X w ,...

## Data mining and database management

Numerous methods for learning from data were developed during the last three decades. However, the interest in data mining has suddenly become intense because of the recent involvement with the field of data base management Berson, Smith, 1997 . Conventional data base management systems DBMS are focused on retrieval of 1. individual records, e.g., -- Display Mr. Smith's payment on February 5 2. statistical records, e.g., -- How many foreign investors bought stock X last month 3....

## Decision tree and DNF learning in finance

Decision-tree methods in finance Langley and Simon 1995 listed some typical financial systems, developed with decision tree methods - Making credit card decisions for card issuing companies through the evaluation of credit card applications. - Advice on share trading for security dealers in six European countries. - Prediction of which overdue mortgages are likely to be paid. - Monitoring excessive claims in health insurance from both clients and providers for different medical...

## Data Mining In Finance

Advances in Relational and Hybrid Methods Institute of Mathematics Russian Academy of Sciences, Russia NEW YORK, BOSTON, DORDRECHT, LONDON, MOSCOW eBook ISBN 0-306-47018-7 Print ISBN 0-792-37804-0 New York, Boston, Dordrecht, London, Moscow Print 2000 KluwerAcademic Publishers New York No part of this eBook may be reproduced or transmitted in any form or by any means, electronic, mechanical, recording, orotherwise, withoutwritten consentfromthe Publisher Created in the United States ofAmerica...

## The scope and methods of the study

This is one of the peculiarly dangerous months to speculate in stocks in. The others are July, January, September, April, November, May, March, June, December, August and February Mark Twain's aphorism became increasingly popular in discussions about a new generation of computing techniques called data mining DM Sullivan at al, 1998 . These techniques are now applied to discover hidden trends and patterns in financial databases, e.g., in stock market data for market prediction. The...

## Statistical autoregression models

Traditional attempts to obtain a short-term forecasting model of a particular financial time series are associated with statistical methods such as ARIMA models. In sections 2.1 and 2.2, ARIMA regression models as typical examples of the statistical approach to financial data mining Box, Jenkins, 1976 Montgomery at al, 1990 are discussed. Section 2.3 contains instance-based learning IBL methods and their financial applications. Another approach, sometimes called regression without models...