scholarly journals Interactive discovery of sequential patterns in time series of wind data

2016 ◽  
Vol 30 (8) ◽  
pp. 1486-1506 ◽  
Author(s):  
Norhakim Yusof ◽  
Raul Zurita-Milla ◽  
Menno-Jan Kraak ◽  
Bas Retsios
Keyword(s):  
Time Series ◽  
Wind Data ◽  
Sequential Patterns

Wind-Aided Recruitment of Canadian Arctic Cisco (Coregonus autumnalis) into Alaskan Waters

1988 ◽  
Vol 45 (5) ◽  
pp. 906-910 ◽  
Author(s):  
Robert G. Fechhelm ◽  
David B. Fissel
Keyword(s):  
Time Series ◽  
Canadian Arctic ◽  
Wind Data ◽  
Commercial Fishery ◽  
Catch Per Unit Effort ◽  
Longshore Currents ◽  
Unit Effort ◽  
Colville River ◽  
Young Of The Year ◽  
Mackenzie River

Summer wind data collected at Barter Island, Alaska, were compared with commercial fishery catches of arctic cisco (Coregonus autumnalis) at the Colville River, Alaska, for the period 1967–85. There was a significant (p = 0.036) association between yearly catch-per-unit-effort and the percent of easterly winds after adjusting for a 5-yr differential in the two time series. Results suggest that young-of-the-year fish which spawn in Canada's Mackenzie River are aided in their westward dispersal into Alaskan waters via wind-driven longshore currents. The greater the prevalence of easterly winds (westerly currents), the greater the recruitment. Increased recruitment manifests itself as an increase in Alaskan commercial fishery catch some 5-yr later when fish have grown to a size that renders them susceptible to commercial nets.

Time Series Mining

2008 ◽  
pp. 14-43
Author(s):  
Wynne Hsu ◽  
Mong Li Lee ◽  
Junmei Wang
Keyword(s):  
Time Series ◽  
Dimension Reduction ◽  
Similarity Measures ◽  
Sequential Patterns ◽  
Sequence Mining ◽  
Periodic Patterns ◽  
Background Material ◽  
Time Series Mining ◽  
Data Discretization

In this chapter, we will first give the background and review existing works in time series mining. The background material will include commonly used similarity measures and techniques for dimension reduction and data discretization. Then we will examine techniques to discover periodic and sequential patterns. This will lay the groundwork for the subsequent three chapters on mining dense periodic patterns, incremental sequence mining, and mining progressive patterns.

scholarly journals Sequential Pattern Mining Algorithm Based on Text Data: Taking the Fault Text Records as an Example

2018 ◽  
Vol 10 (11) ◽  
pp. 4330 ◽  
Author(s):  
Xinglong Yuan ◽  
Wenbing Chang ◽  
Shenghan Zhou ◽  
Yang Cheng
Keyword(s):  
Time Series ◽  
Pattern Mining ◽  
Sequential Pattern Mining ◽  
Sequential Pattern ◽  
Fault Classification ◽  
Sequential Patterns ◽  
Series Data ◽  
Similarity Measurement ◽  
Text Similarity ◽  
Text Data

Sequential pattern mining (SPM) is an effective and important method for analyzing time series. This paper proposed a SPM algorithm to mine fault sequential patterns in text data. Because the structure of text data is poor and there are many different forms of text expression for the same concept, the traditional SPM algorithm cannot be directly applied to text data. The proposed algorithm is designed to solve this problem. First, this study measured the similarity of fault text data and classified similar faults into one class. Next, this paper proposed a new text similarity measurement model based on the word embedding distance. Compared with the classic text similarity measurement method, this model can achieve good results in short text classification. Then, on the basis of fault classification, this paper proposed the SPM algorithm with an event window, which is a time soft constraint for obtaining a certain number of sequential patterns according to needs. Finally, this study used the fault text records of a certain aircraft as experimental data for mining fault sequential patterns. Experiment showed that this algorithm can effectively mine sequential patterns in text data. The proposed algorithm can be widely applied to text time series data in many fields such as industry, business, finance and so on.

scholarly journals Creating Synthetic Wind Speed Time Series for 15 New Zealand Wind Farms

2011 ◽  
Vol 50 (12) ◽  
pp. 2394-2409 ◽  
Author(s):  
Richard Turner ◽  
Xiaogu Zheng ◽  
Neil Gordon ◽  
Michael Uddstrom ◽  
Greg Pearson ◽  
...  
Keyword(s):  
Time Series ◽  
New Zealand ◽  
Wind Speed ◽  
Wind Farm ◽  
Central Government ◽  
Wind Farms ◽  
Practical Method ◽  
Wind Data ◽  
Regulatory Oversight ◽  
Short Period

AbstractWind data at time scales from 10 min to 1 h are an important input for modeling the performance of wind farms and their impact on many countries’ national electricity systems. Planners need long-term realistic (i.e., meteorologically spatially and temporally consistent) wind-farm data for projects studying how best to integrate wind power into the national electricity grid. In New Zealand, wind data recorded at wind farms are confidential for commercial reasons, however, and publicly available wind data records are for sites that are often not representative of or are distant from wind farms. In general, too, the public sites are at much lower terrain elevations than hilltop wind farms and have anemometers located at 10 m above the ground, which is much lower than turbine hub height. In addition, when available, the mast records from wind-farm sites are only for a short period. In this paper, the authors describe a novel and practical method to create a multiyear 10-min synthetic wind speed time series for 15 wind-farm sites throughout the country for the New Zealand Electricity Commission. The Electricity Commission (known as the Electricity Authority since 1 October 2010) is the agency that has regulatory oversight of the electricity industry and that provides advice to central government. The dataset was constructed in such a way as to preserve meteorological realism both spatially and temporally and also to respect the commercial secrecy of the wind data provided by power-generation companies.

Campus Data Analysis Based on Positive and Negative Sequential Patterns

2019 ◽  
Vol 33 (05) ◽  
pp. 1959016 ◽  
Author(s):  
Xiaoqi Jiang ◽  
Tiantian Xu ◽  
Xiangjun Dong
Keyword(s):  
College Students ◽  
Time Series ◽  
Data Analysis ◽  
Sequential Patterns ◽  
Negative Events ◽  
Positive Events ◽  
Mining Algorithm ◽  
Consumption Data ◽  
Academic Grades ◽  
The Relationship

Campus data analysis is becoming increasingly important in mining students’ behavior. The consumption data of college students is an important part of the campus data, which can reflect the students’ behavior to a great degree. A few methods have been used to analyze students’ consumption data, such as classification, association rules, clustering, decision trees, time series, etc. However, they do not use the method of sequential patterns mining, which results in some important information missing. Moreover, they only consider the occurring (positive) events but do not consider the nonoccurring (negative) events, which may lead to some important information missing. So this paper uses a positive and negative sequential patterns mining algorithm, called NegI-NSP, to analyze the consumption data of students. Moreover, we associate students’ consumption data with their academic grades by adding the students’ academic grades into sequences to analyze the relationship between the students’ academic grades and their consumptions. The experimental results show that the students’ academic performance has significant correlation with the habits of having breakfast regularly.

scholarly journals Mining Frequent Sequential Patterns

2021 ◽  
Vol 1 (2) ◽  
pp. 20-37
Author(s):  
Rajae KRIBII ◽  
Youssef FAKIR
Keyword(s):  
Data Mining ◽  
Time Series ◽  
Time Series Data ◽  
Equivalence Classes ◽  
Data Type ◽  
Sequential Patterns ◽  
Sequential Algorithm ◽  
Series Data ◽  
Data Set ◽  
Specific Order

In recent times, the urge to collect data and analyze it has grown. Time stamping a data set is an important part of the analysis and data mining as it can give information that is more useful. Different mining techniques have been designed for mining time-series data, sequential patterns for example seeks relationships between occurrences of sequential events and finds if there exist any specific order of the occurrences. Many Algorithms has been proposed to study this data type based on the apriori approach. In this paper we compare two basic sequential algorithms which are General Sequential algorithm (GSP) and Sequential PAttern Discovery using Equivalence classes (SPADE). These two algorithms are based on the Apriori algorithms. Experimental results have shown that SPADE consumes less time than GSP algorithm.

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