In this paper we develop a framework for the support of temporal data. The concept of a time sequence is introduced, and shown to be an important fundamental concept for representing the semantics of temporal data and for efficient physical organization. We discuss properties of time sequences that allow the treatment of such sequences in a uniform fashion. These properties are exploited in ord...

This paper proposes a novel approach to discover dynamic laws and models represented by simultaneous time differential equations including hidden states from time series data measured in an objective process. This task has not been addressed in the past work though it is essentially important in scientific discovery since any behaviors of objective processes emerge in time evolution. The promis...

We test for an effect of Arizona’s 2007 Legal Arizona Workers Act (LAWA) on the proportion of the state’s population characterized as noncitizen Hispanic. We use the synthetic control method to select a group of states against which Arizona’s population trends can be compared. We document a notable and statistically significant reduction in the proportion of the Hispanic noncitizen population i...

Neo-fuzzy elements are used as nodes for an evolving cascade system. The proposed system can tune both its parameters and architecture in an online mode. It can be used for solving a wide range of Data Mining tasks (namely time series forecasting). The evolving cascade system with neo-fuzzy nodes can process rather large data sets with high speed and effectiveness.

Did the 2007 Legal Arizona Workers Act Reduce the State’s Unauthorized Immigrant Population? We test for an effect of Arizona’s 2007 Legal Arizona Workers Act (LAWA) on the proportion of the state population characterized as foreign-born, as non-citizen, and as non-citizen Hispanic. We use the synthetic control method to select a group of states against which the population trends of Arizona ca...

Article history: Accepted 14 January 2011 JEL classification: D43 J29 J69 L60

In this paper we propose an efficient method for forecasting highly redundant time-series based on historical information. First, redundant inputs and desired outputs are compressed and used to train a single network. Second, network output vectors are uncompressed. Our approach is successfully tested on the hourly temperature forecasting problem.