As companies rely on an ever increasing number of connected devices for their day to operations, a need arises automated anomaly detectors constantly observe crucial device metrics in real time prevent downtime and data loss. production environments tend monitor huge amount these metrics, it prevents current state-of-the-art techniques be deployed as the required computational resources is too ...

The rapid growth of Internet Things (IoT) and sensing technologies has led to an increasing interest in time-series data analysis. In many domains, detecting patterns IoT interpreting these are challenging issues. There several methods analysis that deal with issues such as volume velocity streams. However, analysing the content streams extracting insights from dynamic is still a task. this pap...

Partial periodic patterns are an important class of regularities that exist in a time series. A key property of these patterns is that they can start, stop, and restart anywhere within a series. We classify partial periodic patterns into two types: (i) regular patterns − patterns exhibiting periodic behavior throughout a series with some exceptions and (ii) recurring patterns − patterns exhibit...

A method is proposed to generate an optimal fit of a number of connected linear trend segments onto time-series data. To be able to efficiently handle many lines, the method employs a stochastic search procedure to determine optimal transition point locations. Traditional methods use exhaustive grid searches, which severely limit the scale of the problems for which they can be utilized. The pro...

In this paper, changes in sequences pattern describing damage-sensitive features of an object which undergoes a failure mode are recognized using an immune algorithm. A frequency response change is an effect for various failure modes occurrence. The objective of this paper is to present immune algorithm for pattern recognition which can discover dependencies between failure mode and effect freq...

This article deals with the recognition of recurring multivariate time series patterns modelled sample-point-wise by parametric fuzzy sets. An efficient classification-based approach for the online recognition of incompleted developing patterns in streaming time series is being presented. Furthermore, means are introduced to enable users of the recognition system to restrict results to certain ...

This paper explores a nearest neighbour pattern recognition method for time-series forecasting. A nearest neighbour method (FNNM) based on fuzzy membership values is developed. The main aim of the forecasting algorithm is to make single point forecasts into the future on the basis of past nearest neighbours. The nearest neighbours are selected using a membership threshold value. The results inc...

This research is aimed at presenting a new, pattern recognition-based DSS scheme for the time series model identification. The scheme is based on two principles: pattern matching and inductive learning. Pattern matching is used to classify a pattern of the time series into one of the autoregressive moving-average models. The pattern is obtained from the extended sample autocorrelations of the t...