Classification of time series
Estimation of vegetation cover resilience from satellite time series
... point of view, once the initial cluster centroids (seeds) are chosen, the algorithm computes the de- gree of membership of all feature vectors in all the clus- ters and, then, iteratively computes ...
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Comparison of Hybrid Classifiers for Crop Classification Using Normalized Difference Vegetation Index Time Series: A Case Study for Major Crops in North Xinjiang, China.
... range of classifiers, such as Decision Tree (DT), Support Vector Machine (SVM), Ran- dom Forest (RF), and C5 [14, 21, 22], have been employed for crop type ...advantage of single classifiers and increase ...
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SPECTRAL UNMIXING OF BLENDED REFLECTANCE FOR DENSER TIME-SERIES MAPPING OF WETLANDS
... assessment of the dominant EM classes in the LCF images derived from the observed and blended reflectance data with different EM ...assessment of hard-labeled classification maps using confusion ...
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EEG Classification Using Different Feature Extraction Techniques for Emotion Recognition
... applied of it that is called recognizing emotion from human brain activity, measured by EEG ...applying classification and different feature extraction methods to 300 EEG time series, we ...
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Application of time-series analysis for prediction of molding sand properties in production cycle
... nature of manufacturing processes and permits prediction of future values of the process parameters or production results on the basis of the past data, recorded in regular ...problems ...
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Agent-based distributed time series forecasting system
... results of conducted experiments reveal the perfor- mance of presented ...values of explanatory variable that occur be- fore that point were considered as a historical ...values of the ...
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Brazilian Economic Time Series (BETS): R package
... A função BETS.predict recebe os parâmetros da função forecast do pacote homônimo ou da BETS.grnn.test (a ser tratada adiante, no segundo estudo de caso) e devolve não ape[r] ...
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A Neural Network Approach to Time Series Forecasting
... We also tried with the Mahalanobis distance providing correlation weighted distance but the performance did not improve. Our initial suspect is the high degree of redundancy among predictors. Multicollinearity ...
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GARLIC YIELD FORECASTING BY TIME SERIES MODELS
... aim of the present study was to use time series models in order to realize forecasting garlic yield using data of bulb dry matter from six garlic ...the time series models, data ...
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The Usage of Time Series Control Charts for Financial Process Analysis
... degree of autocorrelation data causes failure of classical Shewhart control ...form of a large number of points outside the regulatory limits in control ...case of continuous processes, ...
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A Survey Paper on Crime Prediction Technique Using Data Mining
... percentage of the data and a predefined level of crime rate, we select a subset of the crime dataset to label; and then based on the predefined level of crime rate, specify a class label to ...
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Identifying environmental controls on vegetation greenness phenology through model-data integration
... % of the land area the correlation coefficient of all LPJmL versions was smaller than ...FAPAR time series of tropical forests do not ex- hibit seasonal cycles and where optical ...
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Investigation of correlation of the variations in land subsidence (detected by continuous GPS measurements) and methodological data in the surrounding areas of Lake Urmia
... north-west of Iran, plays a valuable role in the environment, wildlife and econ- omy of Iran and the region, but now faces great challenges for ...regions of Lake ...objective of this study ...
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DYNAMIC APERIODIC NEURAL NETWORK FOR TIME SERIES PREDICTION
... We ran through all eight markets' stock index data using out myopic KAII neural network and we got different results from different markets. Most of the predicti[r] ...
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Time Series Forecasting of Energy Commodity using Grey Wolf Optimizer
... Prediction of non-renewable natural commodity has experienced major changes for past ...efficiency of the developed GWO forecasting models is measured based on Mean Absolute Percentage Error and prediction ...
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A new class of nonlinear time series models for the evaluation of DSGE models
... Posterior predictive checks based on the QAR(1,1) model detect forms of misspecification that are not detected by comparison with linear time series model... Conclusion[r] ...
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Classification Of Complex UCI Datasets Using Machine Learning And Evolutionary Algorithms
... for classification. The application of GP to classification offers some interesting advantages such as flexibility which allows the technique to be adapted to the needs of each particular ...
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Effects of dating errors on nonparametric trend analyses of speleothem time series
... validity of the esti- mated age–depth curve ...chronology of a lake sediment core dated with radiocarbon and Sp¨otl et ...means of a random number generator using the dating errors for the individual ...
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Cortical Response Similarities Predict which Audiovisual Clips Individuals Viewed, but Are Unrelated to Clip Preference.
... utility of using ISC’s for predicting which audiovisual clips individuals viewed, and to examine the relationship between neural responses to natural stimuli and subjective ...relationship of the EEG ...
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Inferring the Clonal Structure of Viral Populations from Time Series Sequencing.
... prevalence of 50% exists, to determine how often two distinct events would produce identical ...figure of 7% which will get worse as the depth of sequencing increases and some care is needed (see ...
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