D E N N I S C A R N E L O S S I F U R L A N E T O

M S C I N C O M P U T E R S C I E N C E @ U F P R

An analysis of heterogenous ensembles at predictingstock prices of Brazilian power companies

Introduction

Prediction of closing prices

One of the most popular problems in the field

The results can be easily visualized

Same ideas can be applied to other time series

Still an open problem

Challenges

Results are obtained using a different set of tools (weka, matlab, python) and evaluated under different metrics (RMSE, MSE, ...)

Some of the results are too good to be true

Completely different results given a different period in time for the time series

Different classifiers obtain different results in different parts of the same time series

There is no public dataset used for tests on the field

Objective of this study

Not to beat or match the state-of-the-art

Would an ensemble of classifiers help to reduce the variation and improve predictive accuracy?

Not obvious – say we have a good and 2 bad models. What happens?

Fusion of models are not widely used

They have been done, but usually for homogenous ensembles

Pipeline

Data Acquisition & preprocessing

Feature Extraction

Normalization

Feature Selection

Train models

Ensemble

Data Acquisition

Data Acquisition & preprocessing

Feature Extraction

Normalization

Feature Selection

Train models

Ensemble

Feature Extraction

Data Acquisition & preprocessing

Exponential Moving Average

Normalization

Feature Selection

Train models

Ensemble

Normalization

Data Acquisition & preprocessing

Exponential Moving Average

z-score Normalization

Feature Selection

Train models

Ensemble

z-score - distribution

Feature Selection

Data Acquisition & preprocessing

Exponential Moving Average

z-score Normalization

Pearson Correlation

Train models

Ensemble

No Correlation

Inversely Correlated

Directly Correlated

-1 0 +1

Chosen Features:• EMA(Stock): 5, 10, 15• EMA(BVSP): 5, 10, 15

Training Models

Data Acquisition & preprocessing

Exponential Moving Average

z-score Normalization

Pearson Correlation

SVM, Ridge and Random Forests

Ensemble

Ridge (Tikhonov Regularization):• Least-squared error – Reg term

Stacking & Prediction

Data Acquisition & preprocessing

Exponential Moving Average

z-score Normalization

Pearson Correlation

SVM, Ridge and Random Forests

StackingLinear Regression

Dataset

2 stocks – CMIG4, CESP6

BVSP, IEEX and USDBRL

Daily prices between Jan-2008 and Dec-2013

A smal portion of the data points were missing ( ~0.05%)

They were replaced by an interpolated value

How the results were evaluated?

Train/Test routine:

Stored RMSE for each testing iteration

Mean, Std and Median value of RMSEs

% of train/test segments where each model was themost accurate

Results

SVM RidgeRandom

Forest Stacking

CMIG4 16.67% 23.33% 3.33% 55.00%

CESP6 8.33% 16.67% 5.00% 70.00%

Results