Transmitting and storing large volumes of dynamic / time series data collected by modern sensors can represent a significant technological challenge. A possibility to mitigate this challenge is effectively select subset points in order reduce without sacrificing the quality results subsequent analysis. This paper proposes method for adaptively identifying optimal point selection algorithms sens...

Time series forecasting has become an important aspect of data analysis and many real-world applications. However, undesirable missing values are often encountered, which may adversely affect tasks. In this study, we evaluate compare the effects imputation methods for estimating in a time series. Our approach does not include simulation to generate pseudo-missing data, but instead perform on ac...

Particle accelerators are complex facilities that produce large amounts of structured data and have clear optimization goals as well precisely defined control requirements. As such they naturally amenable to data-driven research methodologies. The from sensors monitors inside the accelerator form multivariate time series. With fast preemptive approaches being highly preferred in diagnostics, ap...

Time series data are a collection of chronological observations which generated by several domains such as medical and financial fields. Over the years, different tasks classification, forecasting clustering have been proposed to analyze this type data. also used study effect interventions overtime. Moreover, in many fields science, learning causal structure dynamic systems time is considered a...