Discovering similar time-series patterns with fuzzy clustering and DTW methods

2002 ◽  
Author(s):  
Guoqing Chen ◽  
Qiang Wei ◽  
Hong Zhang
Keyword(s):  
Time Series ◽  
Fuzzy Clustering ◽  
Similar Time

Using fuzzy clustering to address imprecision and uncertainty present in deterministic components of time series

2021 ◽  
pp. 108011
Author(s):  
Marcos Vinícius dos Santos Ferreira ◽  
Ricardo Rios ◽  
Rodrigo Mello ◽  
Tatiane Nogueira Rios
Keyword(s):  
Time Series ◽  
Fuzzy Clustering ◽  
Deterministic Components

scholarly journals Changes in the Occurrence of Late Spring Frost in Poland

Agronomy ◽  
2020 ◽  
Vol 10 (11) ◽  
pp. 1835
Author(s):  
Dariusz Graczyk ◽  
Małgorzata Szwed
Keyword(s):  
Time Series ◽  
Air Temperature ◽  
Growing Season ◽  
Significant Trend ◽  
Similar Time ◽  
Spring Frost ◽  
Late Spring Frost ◽  
Low Probability ◽  
Spring Frosts ◽  
Agricultural Regions

Trends in the appearance of the last spring frost for three thresholds of minimum daily air temperature at the height of 2 m and near the ground were examined for six meteorological stations located in two agricultural regions in Poland. For most time series, the last spring frost, calculated as a consecutive day of the year, showed a statistically significant trend indicating its earlier appearance from 1.6 to about 3.5 days per decade. The date of the last spring frost was also calculated in relation to the ongoing growing season. In this case, few statistically significant changes in the dates of the last frosts were found. The probability of the last spring frost on a specific day of the calendar year and the day of the growing season was also examined for two periods: 1961–1990 and 1991–2020. For low probability levels corresponding to the early dates of the last spring frost, the last frost usually occurred much earlier (6–14 days) in 1991–2020. With the probability levels of 80–90% describing the late occurrence of the last frost with a frequency of once every 5–10 years, at some stations, the last spring frosts occurred at a similar time for both periods.

The unordered time series fuzzy clustering algorithm based on the adaptive incremental learning

2020 ◽  
Vol 38 (4) ◽  
pp. 3783-3791 ◽  
Author(s):  
Huanchun Xu ◽  
Rui Hou ◽  
Jinfeng Fan ◽  
Liang Zhou ◽  
Hongxuan Yue ◽  
...  
Keyword(s):  
Time Series ◽  
Fuzzy Clustering ◽  
Incremental Learning ◽  
Clustering Algorithm ◽  
Fuzzy Clustering Algorithm

A self-adapting multi-LSTM ensemble regression mode for failure prediction of transmission line network from wireless mesh nodes’ data

2020 ◽  
pp. 1-9
Author(s):  
Hongbin Sun ◽  
Mingjun Liu ◽  
Zhejun Qing ◽  
Chandler Miller
Keyword(s):  
Time Series ◽  
Transmission Line ◽  
Time Series Data ◽  
Failure Prediction ◽  
Series Data ◽  
Good Prediction ◽  
Similar Time ◽  
Prediction Ability ◽  
Wireless Mesh ◽  
Ensemble Regression

Transmission lines’ condition monitoring is an important part of smart grid construction. To ensure fast and efficient transmission of data, many mash-based wireless networks devices are adopted to collect status information. Since these nodes are exposed to the natural environment, vulnerable to damage, so it is very necessary to be predicting nodes’ fault. However, these mesh nodes are affected by a variety of complex and time-series factors, and traditional models are difficult to achieve effective failure prediction. To solve this problem, this paper proposes a self-adapting multi-LSTM ensemble regression model for transmission line network’s wireless mesh node failure prediction (MLSTM-FP), through establishes the corresponding relationship between similar time factors and LSTMs, the proposed model can realize multi time series data self-adapting and accurate failure prediction of transmission line network’s wireless mesh nodes, The experimental results show that the proposed method has a good prediction ability than traditional methods.

Information Granulation-Based Fuzzy Clustering of Time Series

2020 ◽  
pp. 1-9
Author(s):  
Hongyue Guo ◽  
Lidong Wang ◽  
Xiaodong Liu ◽  
Witold Pedrycz
Keyword(s):  
Time Series ◽  
Fuzzy Clustering ◽  
Information Granulation
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