A distinguishing feature of symbolic regression using genetic programming is its ability to identify complex nonlinear white-box models. This is especially relevant in practice where models are extensively scrutinized in order to gain knowledge about underlying processes. This potential is often diluted by the ambiguity and complexity of the models produced by genetic programming. In this contribution we discuss several analysis methods with the common goal to enable better insights in the symbolic regression process and to produce models that are more understandable and show better generalization. In order to gain more information about the process we monitor and analyze the progresses of population diversity, building block information, and even more general genealogy information. Regarding the analysis of results, several aspects such as model simplification, relevance of variables, node impacts, and variable network analysis are presented and discussed.

Gaining Deeper Insights in Symbolic Regression

In this study a new approach to quantify qualitative survey data about the direction of change is presented. We propose a data-driven procedure based on evolutionary computation that avoids making any assumption about agents’ expectations. The research focuses on experts’ expectations about the state of the economy from the World Economic Survey in twenty eight countries of the Organisation for Economic Co-operation and Development. The proposed method is used to transform qualitative responses into estimates of economic growth. In a first experiment, we combine agents’ expectations about the future to construct a leading indicator of economic activity. In a second experiment, agents’ judgements about the present are combined to generate a coincident indicator. Then, we use index tracking to derive the optimal combination of weights for both indicators that best replicates the evolution of economic activity in each country. Finally, we compute several accuracy measures to assess the performance of these estimates in tracking economic growth. The different results across countries have led us to use multidimensional scaling analysis in order to group all economies in four clusters according to their performance.

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A new approach for the quantification of qualitative measures of economic expectations

Financial networks have been extensively studied as examples of real world complex networks. In this paper, we establish and study the network of venture capital (VC) firms in China. We compute and analyze the statistical properties of the network, including parameters such as degrees, mean lengths of the shortest paths, clustering coefficient and robustness. We further study the topology of the network and find that it has small-world behavior. A multiple linear regression model is introduced to study the relation between network parameters and major regional economic indices in China. From the result of regression, we find that, economic aggregate (including the total GDP, investment, consumption and net export), upgrade of industrial structure, employment and remuneration of a region are all positively correlated with the degree and the clustering coefficient of the VC sub-network of the region, which suggests that the development of the VC industry has substantial effects on regional economy in China.

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Characteristics of Venture Capital Network and Its Correlation with Regional Economy: Evidence from China.

This article describes the architecture and implementation of the genetic programming (GP) framework of HeuristicLab In particular we focus on the core design goals, namely extensibility, usability, and performance optimization and explain our approach to reach these goals The overall design, the encoding, interpretation, and evaluation of programs is described and code examples are given to explain core aspects of the framework HeuristicLab is available as open source software at http://devheuristiclabcom

On the architecture and implementation of tree-based genetic programming in HeuristicLab

The main objective of this study is twofold. First, we propose an empirical modelling approach based on genetic programming to forecast economic growth by means of survey data on expectations. We use evolutionary algorithms to estimate a symbolic regression that links survey-based expectations to a quantitative variable used as a yardstick, deriving mathematical functional forms that approximate the target variable. The set of empirically-generated proxies of economic growth are used as building blocks to forecast the evolution of GDP. Second, we use these estimates of GDP to assess the impact of the 2008 financial crisis on the accuracy of agents’ expectations about the evolution of the economic activity in four Scandinavian economies. While we find an improvement in the capacity of agents’ to anticipate economic growth after the crisis, predictive accuracy worsens in relation to the period prior to the crisis. The most accurate GDP forecasts are obtained for Sweden.

/pdf/evolutionary-computation-for-macroeconomic-forecasting-216tjfor2u.pdf

Evolutionary Computation for Macroeconomic Forecasting

Macro-economic models describe the dynamics of economic quantities. The estimations and forecasts produced by such models play a substantial role for financial and political decisions. In this contribution we describe an approach based on genetic programming and symbolic regression to identify variable interactions in large datasets. In the proposed approach multiple symbolic regression runs are executed for each variable of the dataset to find potentially interesting models. The result is a variable interaction network that describes which variables are most relevant for the approximation of each variable of the dataset. This approach is applied to a macro-economic dataset with monthly observations of important economic indicators in order to identify potentially interesting dependencies of these indicators. The resulting interaction network of macro-economic indicators is briefly discussed and two of the identified models are presented in detail. The two models approximate the help wanted index and the CPI inflation in the US.

Macro-Economic Time Series Modeling and Interaction Networks

Macro-economic time series modeling and interaction networks

In this paper we analyze the dynamics of the predictability and variable interactions in financial data of the years 2007–2014. Using a sliding window approach, we have generated mathematical prediction models for various financial parameters using other available parameters in this data set. For each variable we identify the relevance of other variables with respect to prediction modeling. By applying sliding window machine learning we observe that changes of the predictability of financial variables as well as of influence factors can be identified by comparing modeling results generated for different periods of the last 8 years. We see changes of relationships and the predictability of financial variables over the last years, which corresponds to the fact that relationships and dynamics in the financial sector have changed significantly over the last decade. Still, our results show that the predictability has not decreased for all financial variables, indeed in numerous cases the prediction quality has even improved.

Stefan Fink

Papers

Macro-Economic Time Series Modeling and Interaction Networks

Macro-economic time series modeling and interaction networks

Dynamics of Predictability and Variable Influences Identified in Financial Data Using Sliding Window Machine Learning