Non linear time series
Linear and Non-linear time series analysis: forecasting financial markets
... A analise de séries temporais na area financeira tem atraido especial atenção nos últimos anos. Os mercados financeiros são exemplos de sistemas com um comportamento complexo e, por vezes, a previsão de séries temporais ...
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Temperature changes between neighboring days and mortality in summer: a distributed lag non-linear time series analysis.
... lag non-linear model has been proposed to simultaneously investigate the delayed effects and the non-linear exposure–response relationship ...
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Integration of Attributes from Non-Linear Characterization of Cardiovascular Time-Series for Prediction of Defibrillation Outcomes.
... Receiver Operating Characteristic (ROC) analysis was used to evaluate reliability of all mod- els by calculating the area under the curve (AUC). Accuracy was calculated as the average per- centage, over all ...
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Forecasting of a Non-Seasonal Tourism Time Series with ANN
... tourism time series used in this work contrary to usual tourism time series is non ...no linear problems and were used to make predictions of this time series for ...
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Inverse Correlations for Multiple Time Series and Gaussian Random Fields and Measures of Their Linear Determinism
... Table 5 shows the simulated means and standard errors of the estimated indices, Â L , in 1000 realizations, each of size M=128, for these three misspecified models. The simulated means for the two isotropic models, ...
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On the non-negative first-order exponential bilinear time series model
... stationary time series the class of linear models with finite variance, which includes ARMA models, plays a central ...in time, and high threshold exceedances ap- pearing in ...Various ...
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Time Alignment Measurement for Time Series
... two series in order to use the optimal alignment ...real time series ...inate time warping differences of human repetitive ...although time series demonstrated amplitude ...
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Network inference : extension of linear programming model for time-series data
... To infer the network using DDEPN, we formatted the data using the function format_ddepn from the ddepn R package and defined the following settings: popula- tion size 500, maximum number of iterations 1000, crossover ...
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Forecasting non-stationary diarrhea, acute respiratory infection, and malaria time-series in Niono, Mali.
... disease time-series often i ) suffer from non-stationarity; ii ) exhibit large inter-annual plus seasonal fluctuations; and, iii ) require disease-specific tailoring of forecasting ...consultation ...
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Bayesian Outlier Detection in Non-Gaussian Autoregressive Time Series
... count time series. These time series arise in a wide variety of fields including: telecommunications, actuarial science, computer science, economics, epidemiology, finance, hydrology, ...
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On reconstruction of time series in climatology
... the time series which contains simultaneous observations of both scalar series with subsequent application of the model to restore the shorter one into the ...of time series analysis ...
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GARLIC YIELD FORECASTING BY TIME SERIES MODELS
... Time series models have been used to determine the final yield of some cultures of commercial importance, such as sugarcane (NASCIMENTO et al., 2009) and coffee (CARVALHO et al., 2005), when well adjusted, ...
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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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Data imputation analysis for Cosmic Rays time series
... siders that all variables in a dataset have multivariate normal distribution (MVN), using mean and covariance to summarize data. The imputation is carried out ran- domly, so, it failed to represent the observed GCR data. ...
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A Neural Network Approach to Time Series Forecasting
... We present an improved algorithm, based on GRNN, for the time series forecasting. GRNN is a neural network proposed by Donald F. Specht in 1991 [3]. This algorithm has a number of advantages over competing ...
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Change-point analysis in environmental time series
... over time (each time series consists at most of 156 observations) presented changes in mean and/or variance in the series (in particular between 2004 and ...
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Time Series Analysis based on Complex Networks
... the time series on different time intervals and the expansion of visibility concepts, for instance, considering directed edges or weighted connections that reflect the visibility angle (this might ...
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Outliers detection in integer-valued time series
... Recently, [7] suggested a Bayesian approach in order to detect additive outliers in Poisson first-order INteger-valued AutoRegressive, INAR(1), modeis. The clnss of INAR modeis for time [r] ...
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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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