Causal inference uses observational data to infer the causal structure of the data generating system. We study a class of restricted Structural Equation Models for time series that we call Time Series Models with Independent Noise (TiMINo). These models require independent residual time series, whereas traditional methods like Granger causality exploit the variance of residuals. This work conta...

Time-series prediction is important in physical and financial domains. Pattern recognition techniques for time-series prediction are based on structural matching of the current state of the time-series with previously occurring states in historical data for making predictions. This paper describes a Pattern Modelling and Recognition System (PMRS) which is used for forecasting benchmark series a...

Assuming a good embedding and additive noise, the traditional approach to time-series embedding prediction has been to predict pointwise by (usually linear) regression of the k-nearest neighbors; no good mathematics has been previously developed to appropriately select the model (where to truncate Taylor’s series) to balance the conflict between noise fluctuations of a small k, and large k data...

A general method is developed by using nonstandard analysis for formulating and proving a theorem about upper Banach density parallel to each theorem about Shnirel'man density or lower asymptotic density. There are many interesting results about Shnirel'man density or lower asymptotic density (see [4, Chapter 1] for example) in additive number theory. There are also a few interesting results ab...

In this paper, the performance of Empirical Mode Decomposition (EMD) applied in biomedical signals is investigated and especially it is considered the case of electrocardiogram (ECG). Synthetic ECG signals corrupted with White Gaussian Noise (WGN) as well as real ECG records are employed and a variety of time series lengths is processed with EMD in order to extract the Intrinsic Mode Functions ...

The factors associated with the development of heart failure and of atrial fibrillation in elderly patients were studied in a prospective clinico-pathological series of 171 cases. Multiple logistic analyses allowed the contributions of multiple factors to be assessed simultaneously. Senile cardiac amyloidosis, ischaemic heart disease and atrial fibrillation were significantly associated with he...

The article describes the method of forecasting time series of cash withdrawals in ATMs. The hybrid model is based on two methods: Holt-Winters additive method and Markov chainbased model. The combination occurs with the help of weight coefficients which are calculated on the basis of work of each model. Holt-Winters method forecasts time series with trend and seasonal variations. Markov chain ...

It has been reported in the previous papers of this series that in the eastern United States and Japan there is a north-to-south cline of additive genetic variance of viability and that the amount of the additive genetic variance in the northern population can be explained by mutation-selection balance. To determine whether or not the difference in the genetic variation in northern and southern...

Application of nonand semiparametric regression techniques to high dimensional time series data have been hampered due to the lack of effective tools to address the “curse of dimensionality”. Under rather weak conditions, we propose spline-backfitted kernel estimators of the component functions for the nonlinear additive time series data that is both computationally expedient so it is usable fo...