A data mining method for synthesizing multiple time series is presented. Based on a single time series algorithm, the method embeds multiple time series into a phase space. The reconstructed state space allows temporal pattern extraction and local model development. Using an a priori data mining objective, an optimal local model is chosen for short-term forecasting. For the same sampling period...

Brain science is a frontier research area with great promise for understanding, preventing, and treating multiple diseases affecting millions of patients. Its key task of reconstructing neuronal brain connectivity poses unique Big Data Analysis challenges distinct from those in clinical or "-omics" domains. Our goal is to understand the strengths and limitations of reconstruction algorithms, me...

1 Singh, S. "Forecasting using a Fuzzy Nearest Neighbour Method", Proc. 6th International Conference on Fuzzy Theory and Technology , Fourth Joint Conference on Information Sciences (JCIS'98), North Carolina, vol. 1, pp.80-83, 1998 (23-28 October ,1998) ABSTRACT This paper explores a nearest neighbour pattern recognition method for time-series forecasting. A nearest neighbour method (FNNM) base...

In intensive care physiological variables of the critical-ly ill are measured and recorded in short time intervals. The existing alarm systems based on fixed thresholds produce a large number of false alarms. Usually the change of a variable over time is more informative than one pathological value at a particular time point. Intelligent alarm systems which detect important changes within a phy...

Accurately quantifying the variation of urban green space is the prerequisite for fully understanding its ecosystem services. However, knowledge about the spatiotemporal dynamics of urban green space is still insufficient due to multiple challenges that remain in mapping green spaces within heterogeneous urban environments. This paper uses the city of Hangzhou to demonstrate an analysis methodo...

We demonstrate simultaneous prediction of two independent Santa-Fe time series using a single-longitudinal mode semiconductor ring laser with optical feedback. Our results indicate that a prediction with errors comparable to the state-ofthe-art can be achieved for each time series despite the two tasks are computed simultaneously. I by the way that the brain processes the information, computati...

Data series that contain patterns are the expression of a set of rules that specify the pattern. In cases where the data series is known but the rules are not known, the Kasai algorithm can analyze the data pattern and produce the complete set of rules that described the data pattern observed to date.

let $k$ be a field of characteristic$p>0$, $k[[x]]$, the ring of formal power series over $ k$,$k((x))$, the quotient field of $ k[[x]]$, and $ k(x)$ the fieldof rational functions over $k$. we shall give somecharacterizations of an algebraic function $fin k((x))$ over $k$.let $l$ be a field of characteristic zero. the power series $finl[[x]]$ is called differentially algebraic, if it satisfies...

Integer time series are often subject to constraints on the aggregation of the features of all occurrences of some pattern within the series. For example, the number of inflexions may be constrained, or the sum of the peak maxima, or the minimum of the valley widths. Many time-series constraints can be described by transducers. The output alphabet of such a transducer consists of symbols that d...

in this paper, i develop three forecasting models: namely structural, times series, and artificial neural networks; to forecast iranian inflation rates. the structural model uses aggregate demand and aggregate supply approach, the time series model is based on the standard arlma technique, and the artificial neural network applies multi-layer back propagation model the latter, which is rooted i...