Time Series Data Mining
Time-Series Data Mining in Transportation: A Case Study on Singapore Public Train Commuter Travel Patterns
... Time-series data refers to data collected in a routine, continuous and sequential ...of data, which typically accompanied with a timestamp, has always been collected by businesses and ...
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A New Similarity Measure for Time Series Data Mining Based on Longest Common Subsequence
... appropriate time series similarity measurement method for Time series data mining, which is important effective factor in the quality of ...of time series is one of ...
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Forecasting Chaotic Stock Market Data using Time Series Data Mining
... them data mining techniques have been successfully shown to generate high forecasting accuracy of stock price ...for mining large stocks of time series ...with time series ...
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The Prediction Algorithm Based on Fuzzy Logic Using Time Series Data Mining Method
... Data mining is the process of discovering useful pattern in data that are hidden and important ...applied data mining concepts to finding patterns in time series include ...
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Fault prediction of fan bearing using time series data mining
... a time series. A novel method based on time series data mining is proposed for the prediction of fan bearing ...The time series, which is formed by large numbers of ...
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Time Series Data Mining in Real Time Surface Runoff Forecasting through Support Vector Machine
... Rainfall-runoff brings the most important role in the aspects of human life in all types of weather happenings. This is natural climatic phenomenon whose prediction is tough and demanding. Accurate information on ...
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Transition in Time Series Data Mining on Correlated Items
... [1] where rules were generated by finding candidates and verifying that their support and confidence meet a predefined minimum support and confidence. This approach was greatly limited because of its redundancy in ...
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MINING TIME SERIES DATA BASED UPON CLOUD MODEL
... spatial data have the time dimension, and will change over ...time. Time series space contains the time dimension in spatial association characteristics and can get time ...
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Text‑Mining in Streams of Textual Data Using Time Series Applied to Stock Market
... in data over time. The input data may be either put into one big set and processed in bulk or divided into smaller parts to be processed ...the data streams are important at certain times – ...
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Data Mining Algorithm for Off-Group Points on Noise Polluted Time Series Based on ESO
... Abstract – The practically measured signals always contain wild values deviating far from true values. How to remove these wild values is an important research project for data mining of off-group points. ...
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Mining previously unknown patterns in time series data
... n data elements, a partition-based clustering method assign each data element into k groups where k ≤ n so that each group contains at least one ...each data element is then allocated to its nearest ...
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Time Series Mining: Shapelet Discovery, Ensembling, and Applications
... The first shapelet algorithm is the decision tree shapelet approach that was originally proposed by Ye and Keogh [ 49 ]. The idea is to initially mine shapelets from the dataset in a brute force fashion and uses the ...
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Mining Relevant and Extreme Patterns on Climate Time Series with CLIPSMiner
... on time series. CLIPSMiner tracks time series of continuous data and sets control points as a quantization ...the time occurrence of the events, organizing the pieces quantized ...
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A Literature Review on Big Data and Time Series
... of data that they need to handle. Huge amount of data known as big data denotes large datasets that have high velocity and variety that makes them hard to handle using some known techniques and ...
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Data Abstraction for Visualizing Large Time Series
... Numeric time series is a class of data consisting of chronologically ordered observations represented by numeric ...the data in various domains, such as financial, medical, and scientific, are ...
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Mining the key regulatory genes of chicken inosine 5′-monophosphate metabolism based on time series microarray data
... IMP content at 10 wk dropped sharply and then showed subsequent significant increase at 12 wa. Kehua et al. [17] found Rugao chicken growth rate slowed down after 7 wk, and shrinked faster from 10 wk to 12 wk than from 8 ...
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Energy Load Mining Using Univariate Time Series Analysis
... Smoothing techniques used for reducing of canceling the effect due to random variation in time series data. This technique, when properly applied, provide a clearer view of the true underlying ...
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Detection of Outliers in Time Series Data
... I wish to express my gratitude to many people who have provided me with crucial help and support while carrying out the research for this thesis. Without the help and encouragement that my advisor, Dr. George Corliss has ...
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Applied temporal Rule Mining to Time Series
... rule mining framework we used the so called Cylinder-Bell-Funnel data set [18, 13, ...a time series by con- catenating 90 instances of the cylinder (C), bell (B) and funnel (F) patterns using ...
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Stock market time series forecasting with data mining methods
... original time series rather than the input ...original time series into a finite number of IMFs, which are easier to ...original time series (Cheng and Wei, ...a data ...
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