نتایج جستجو برای: series data
تعداد نتایج: 2665883 فیلتر نتایج به سال:
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...
It is common to define a change in health status or in a disease state on the basis of a sustained rise (or decline) in a biomarker over time. However, such observations are often subject to important variability unrelated to the underlying biologic process. The authors propose a method to evaluate rules that define an event on the basis of consecutive increases (or decreases) in the observatio...
The time series is a collection of observation data that are arranged according to time. The main purpose of setting up a time series is to predict future values. The first step in time series data is graphed. Using graphs can provide general information such as uptrend or downtrend, seasonal patterns, periodic presence, and outliers in time series graphs. After graphing the data, if a good for...
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.
OF THE DISSERTATION Exact Primitives for Time Series Data Mining
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