pattern discovery fuzzy time series

7/28/2019 Pattern Discovery Fuzzy Time Series

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Pattern Discovery of Fuzzy Time

Series for Financial Prediction-IEEE Transaction of Knowledge and Data Engineering

Presented by Hong YanchengFor COMP630P, Spring 2009


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Outline

Introduction and target problem

Background knowledge and related work

Modeling the candlestick pattern

Candlestick pattern for financial prediction

Experiments and applications

Conclusion and Discussion


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Problems with existing stock

prediction tools

A lot of tools exists for predicting stock price

Artificial Neural Network, SVM, NeuroFuzzy,

Nave Bayes and so on

Three major problems with these tools Training process is nontrivial and training result

cannot be further used for other target

Prediction results are incomprehensible

Hard for user to tuning the parameters

Gap exists between prediction result and

investment decision

Improving prediction VS buy/sell decision


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Target problem

Data preprocessing are needed before

applying various of techniques

Data mining, machine learning & pattern

recognition Good knowledge representation method can

assist investors

Knowledge-based method to transfer

financial data to comprehensible rules and

visual patterns


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Outline








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Japanese Candlestick Theory

Four general ways of represent stock price

fluctuation

Original daily fluctuation

Single close price Bar chart

Candlestick chart

More visual information


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Fuzzy Time Series

Fuzzy time series

Assume U is the universe of discourse,

where U = {x1, x2,, xn}. A fuzzy set Ai of U

is defined byAi = Ai

(x1)/x1 + Ai(x2)/x2+ + Ai

(xn)/xn

where Ai(xk) is membership function of the

fuzzy set Ai ,Ai: U -> [0,1]


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Outline








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Fuzzy candlestick pattern

A fuzzy candlestick pattern is composed of

related fuzzy candlestick lines in a period

A fuzzy candlestick line has seven parts

Sequence, open style, close style, upper shadow,body, body color and lower shadow

Sequence defines the location of the candlestick

Open/Close style model the relationship between

consecutive candlestick lines


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Candlestick line modeling

Modeling the length of shadow and body

Four linguistic variables EQUAL, SHORT,

MIDDLE and LONG indicate the fuzzy sets

of length Lupper= ([high MAX(open, close)]/open) * 100

Llower= ([MIN(open, close) - low]/open) * 100

Lbody = ([MAX(open, close) MIN(open, close)]/open)

* 100


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The membership function of four fuzzy sets

are shown as follows

The range is set to (0, 14) because the Taiwan

stock price limitation


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The body color is defined by three terms

BLACK, WHITE and CROSS

If openclose > 0 then body color is BLACK

If openclose < 0 then body color is WHITE If openclose = 0 then body color is CROSS


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The open/close style is another important

feature

Five linguistic variables LOW, EQUAL_LOW,

EQUAL, EQUAL_HIGH, HIGH indicate fuzzysets of open/close style


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Trend modeling

Two linguistic variables are used to model

the trends before and afterthe candlestick

pattern

previous trend is represented by weeklycandlestick line

Six fuzzy sets are used to define the trend

CROSS, EQUAL, WEAK, NORMAL, STRONG,

and EXTREME

BEARISH and BULLISH define the body color


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Trend modeling

Following trend is derived from the variation

of close price

(Closet+n Closet)/ Closet * 100

Closet+n and Closet mean the close price at dayt+n and day t respectively

n is a user-defined parameter


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Outline








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Three major pattern recognition

problems

Sensing problem

Measured values are open, close, high, low

Feature extraction problem

Fuzzy candlestick patterns

Pattern classification problem

Can be determined by user


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Forecast procedure

Step 1

Calculate the variation percentage between two

close prices.

Use the minimum increase Imin and maximumincrease Imax to define the universe of discourse

UoD = [IminD1, Imax +D2]

E.g. Imin = -5.83, Imax = 7.66 then UoD = [-6, 8]

Step 2 Partition UoD into several intervals

E.g. partition [-6, 8] into seven intervals [-6, -4], [-

4, -2], , [6, 8]


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Forecast procedure

Step 3

Define fuzzy sets on the UoD associate with the

intervals in step 2

Step 4 Fuzzifying the values calculated in step 1

If v ux, and there is Ay in which maximum

membership function occurs at ux, v is translate

to Ay


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Forecast procedure

Step 5

Calculate all the candlestick patterns

Step 6

Refine extracted patterns, identify importantattributes

Step 7

Select pattern for forecasting based on

probability P(Ax |Py )

Statistic T = Count(Py Ax)/Count(Py) as the

threshold to select the patterns


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Forecast procedure

Step 8

Forecast the trend follows

Rule 1: test pattern not found, set variation v to 0

Rule 2: test pattern found, set variation v toarithmetic average of midpoints of matched

patterns

Forecast = close + close * v

Step 9 Evaluate the forecasting

MSE = (Forecasti - Actuali)2 / N


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Outline








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Experiments and Applications

The experiments are conducted based on

TAIEX index from 2004-01-02 to 2005-01-31

and 2330(TSMC) from 1997-10-23 to 2002-

12-25


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Experiment for TAIEX index


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Experiment results for TAIEX


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Problems with existing stock

prediction tools

Three major problems with these tools

Training process is nontrivial and training result

cannot be further used for other target

Prediction results are incomprehensible Hard for user to tuning the parameters

Gap exists between prediction result and

investment decision

Improving prediction VS buy/sell decision


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Experiment with 2330

(TSMC)

The focus is to find the buying

time of the stock The rule is: IF T>0.5 and the

following trend is

STRONG_INCREASE or

EXTREME_INCREASE

THEN select the pattern

5-day return is 2.9% on

average


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Fuzzy modifier can be implemented to help

user tuning the parameters

ABOVE, BELOW, PLUS, VERY, EXTREMELY,

MORE_OR_LESS, SOMEWHAT, and NOT E.g. STRONG_BEARISH and

EXTREME_BEARISH can be merged by ABOVE

STRONG_BEARISH


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Outline








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Pros

Knowledge-based method to represent the

financial time series and to facilitate the

knowledge discovery

Comprehensible, computable and visual

Can be used directly or as data preprocess

Cons

Time complexity How many candlestick lines for a pattern


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Thanks for listening


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Q & A

pattern discovery fuzzy time series

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