2 research outputs found
Stock Portfolio Prediction by Multi-Target Decision Support
Investing in the stock market is a complex process due to its high volatility caused by factors as exchange rates, political events, inflation and the market history. To support investor's decisions, the prediction of future stock price and economic metrics is valuable. With the hypothesis that there is a relation among investment performance indicators, the goal of this paper was exploring multi-target regression (MTR) methods to estimate 6 different indicators and finding out the method that would best suit in an automated prediction tool for decision support regarding predictive performance. The experiments were based on 4 datasets, corresponding to 4 different time periods, composed of 63 combinations of weights of stock-picking concepts each, simulated in the US stock market. We compared traditional machine learning approaches with seven state-of-the-art MTR solutions: Stacked Single Target, Ensemble of Regressor Chains, Deep Structure for Tracking Asynchronous Regressor Stacking, Deep Regressor Stacking, Multi-output Tree Chaining, Multi-target Augment Stacking and Multi-output Random Forest (MORF). With the exception of MORF, traditional approaches and the MTR methods were evaluated with Extreme Gradient Boosting, Random Forest and Support Vector Machine regressors. By means of extensive experimental evaluation, our results showed that the most recent MTR solutions can achieve suitable predictive performance, improving all the scenarios (14.70% in the best one, considering all target variables and periods). In this sense, MTR is a proper strategy for building stock market decision support system based on prediction models
Towards meta-learning for multi-target regression problems
Several multi-target regression methods were devel-oped in the last years
aiming at improving predictive performanceby exploring inter-target correlation
within the problem. However, none of these methods outperforms the others for
all problems. This motivates the development of automatic approachesto
recommend the most suitable multi-target regression method. In this paper, we
propose a meta-learning system to recommend the best predictive method for a
given multi-target regression problem. We performed experiments with a
meta-dataset generated by a total of 648 synthetic datasets. These datasets
were created to explore distinct inter-targets characteristics toward
recommending the most promising method. In experiments, we evaluated four
different algorithms with different biases as meta-learners. Our meta-dataset
is composed of 58 meta-features, based on: statistical information, correlation
characteristics, linear landmarking, from the distribution and smoothness of
the data, and has four different meta-labels. Results showed that induced
meta-models were able to recommend the best methodfor different base level
datasets with a balanced accuracy superior to 70% using a Random Forest
meta-model, which statistically outperformed the meta-learning baselines.Comment: To appear on the 8th Brazilian Conference on Intelligent Systems
(BRACIS