Streaming Synthetic Time Series for Simulated Condition Monitoring
نویسندگان
چکیده
منابع مشابه
Condition Evaluation for Speculative Systems: a Streaming Time Series Case
Application systems often need to react with certain actions whenever some preset conditions are satisfied. In many cases, the evaluation of these conditions takes long time, but some prediction of the results can be obtained rather quickly. In this situation, speculation may be a good idea. That is, the system takes predictions (speculation) to prepare (such as prefetch) for the possible react...
متن کاملClustering Techniques for Streaming Time Series Data
Clustering streaming time series also referred to as a multivariate streaming time series clustering has been researcher’s target for a long time. Yet so far there are many issues banning them to reach a nearly optimal solution. In this paper we investigate these works pointing to some of the issues focusing on the parameters and give some points to be accounted for future resaerches.
متن کاملOn-Line Dynamic Time Warping for Streaming Time Series
Dynamic Time Warping is a well-known measure of dissimilarity between time series. Due to its flexibility to deal with non-linear distortions along the time axis, this measure has been widely utilized in machine learning models for this particular kind of data. Nowadays, the proliferation of streaming data sources has ignited the interest and attention of the scientific community around on-line...
متن کاملSpatiotemporal analysis of remotely sensed Landsat time series data for monitoring 32 years of urbanization
The world is witnessing a dramatic shift of settlement pattern from rural to urban population, particularly in developing countries. The rapid Addis Ababa urbanization reflects this global phenomenon and the subsequent socio-economic and environmental impacts, are causing massive public uproar and political instability. The objective of this study was to use remotely sensed Landsat data to iden...
متن کاملAlgorithms for Segmenting Time Series
As with most computer science problems, representation of the data is the key to ecient and eective solutions. Piecewise linear representation has been used for the representation of the data. This representation has been used by various researchers to support clustering, classication, indexing and association rule mining of time series data. A variety of algorithms have been proposed to obtain...
متن کاملذخیره در منابع من
با ذخیره ی این منبع در منابع من، دسترسی به آن را برای استفاده های بعدی آسان تر کنید
ژورنال
عنوان ژورنال: IFAC-PapersOnLine
سال: 2018
ISSN: 2405-8963
DOI: 10.1016/j.ifacol.2018.08.391