scholarly journals Whole Time Series Data Streams Clustering: Dynamic Profiling of the Electricity Consumption

Entropy ◽  
2020 ◽  
Vol 22 (12) ◽  
pp. 1414
Author(s):  
Krzysztof Gajowniczek ◽  
Marcin Bator ◽  
Tomasz Ząbkowski
Keyword(s):  
Time Series ◽  
Data Streams ◽  
Smart Grids ◽  
Time Series Data ◽  
Electricity Consumption ◽  
Large Data ◽  
Individual Characteristics ◽  
Series Data ◽  
Underlying Distribution ◽  
Sliding Windows

Data from smart grids are challenging to analyze due to their very large size, high dimensionality, skewness, sparsity, and number of seasonal fluctuations, including daily and weekly effects. With the data arriving in a sequential form the underlying distribution is subject to changes over the time intervals. Time series data streams have their own specifics in terms of the data processing and data analysis because, usually, it is not possible to process the whole data in memory as the large data volumes are generated fast so the processing and the analysis should be done incrementally using sliding windows. Despite the proposal of many clustering techniques applicable for grouping the observations of a single data stream, only a few of them are focused on splitting the whole data streams into the clusters. In this article we aim to explore individual characteristics of electricity usage and recommend the most suitable tariff to the customer so they can benefit from lower prices. This work investigates various algorithms (and their improvements) what allows us to formulate the clusters, in real time, based on smart meter data.

scholarly journals Hybrid Algorithm for Anomaly Removal in Time Series Data Mining

2021 ◽  
Author(s):  
Abdul Razaque ◽  
Marzhan Abenova ◽  
Munif Alotaibi ◽  
Bandar Alotaibi ◽  
Hamoud Alshammari ◽  
...  
Keyword(s):  
Data Mining ◽  
Time Series ◽  
Hybrid Algorithm ◽  
Time Series Data ◽  
State Of The Art ◽  
Large Data ◽  
Series Data ◽  
Multidimensional Data ◽  
Search Problem ◽  
Short Text

Time series data are significant and are derived from temporal data, which involve real numbers representing values collected regularly over time. Time series have a great impact on many types of data. However, time series have anomalies. We introduce hybrid algorithm named novel matrix profile (NMP) to solve the all-pairs similarity search problem for time series data. The proposed NMP inherits the features from two state-of-the art algorithms: similarity time-series automatic multivariate prediction (STAMP), and short text online microblogging protocol (STOMP). The proposed algorithm caches the output in an easy-to-access fashion for single- and multidimensional data. The proposed NMP algorithm can be used on large data sets and generates approximate solutions of high quality in a reasonable time. The proposed NMP can also handle several data mining tasks. It is implemented on a Python platform. To determine its effectiveness, it is compared with the state-of-the-art matrix profile algorithms i.e., STAMP and STOMP. The results confirm that the proposed NMP provides higher accuracy than the compared algorithms.

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