scholarly journals Similarity Measures and Dimensionality Reduction Techniques for Time Series Data Mining

10.5772/49941 ◽  
2012 ◽  
Author(s):  
Carmelo Cassisi ◽  
Placido Montalto ◽  
Marco Aliotta ◽  
Andrea Cannata ◽  
Alfredo Pulvirenti
Keyword(s):  
Data Mining ◽  
Time Series ◽  
Dimensionality Reduction ◽  
Time Series Data ◽  
Similarity Measures ◽  
Series Data ◽  
Time Series Data Mining ◽  
Reduction Techniques ◽  
Dimensionality Reduction Techniques

Interactive Visualization Adopting Dimensionality Reduction Techniques for Pattern Recognition in Large Temporal Datasets

2021 ◽  
Author(s):  
◽  
Mohammed Ali
Keyword(s):  
Machine Learning ◽  
Time Series ◽  
Dimensionality Reduction ◽  
Visual Analytics ◽  
Large Time ◽  
Time Series Data ◽  
Machine Learning Algorithms ◽  
Series Data ◽  
Reduction Techniques ◽  
Dimensionality Reduction Techniques

In this thesis, we focus on time-series data, which is commonly used by domain experts in different domains to explore and understand phenomena or behaviors under consideration, as-sisting them in making decisions, predicting the future or solving problems. Utilizing sensor devices is one of the common ways of collecting time-series data. These devices collect large volumes of raw data, including multi-dimensional time-series data, and each value is associated with the time-stamp corresponding to when it was recorded. However, finding interesting pat-terns or behaviors in a large amount of data is not simple due to the nature of the data and other challenges related to its size and scalability, high dimensionality, complexity, representation, and unique structure.Researchers tend to use time-series chart visualization, which is usually unsuitable because of the small screen resolution which cannot accommodate the large size of the data. Hence, occlusion and overplotting issues occur, limiting or complicating the exploration and analysis tasks. Another challenge concerns the labeling of patterns in large time-series data, which is time-consuming and requires a great deal of expert knowledge.These issues are addressed in this thesis to improve the exploration, analysis and presen-tation of time-series data and enable users to gain insights into large and multi-dimensional time-series datasets using a combination of dimensionality reduction techniques and interac-tive visual methods. The provided solutions will help researchers from various domains who deal with large and multi-dimensional time-series data to efficiently explore and analyze such data with little effort and in record time.Initially, we explore the area of integration between machine learning algorithms and inter-active visualization techniques for exploring and understanding time-series data, specifically looking at clustering and classification for time-series data in visual analytics. The survey is considered to be a valuable guide for both new researchers and experts in the emerging field of integrating machine learning algorithms into visual analytics.Next, we present a novel approach that aims to explore, analyze, and present large temporal datasets through one image. The proposed approach uses a sliding window and dimensionality reduction techniques to depict a large time-series data as points into a 2D scatter plot. The approach provides novel solutions to many pattern discovery issues and can deal with both univariate and multivariate time-series data.Following this, our proposed approach is combined with both visualization and interaction techniques into one system called TimeCluster, which is a visual analytics tool allowing users to visualize, explore and interact with large time-series data. The system addresses different issues such as anomaly detection, the discovery of frequent patterns, and the labeling of in-teresting patterns in large time-series data all in a single system. We deploy our system with different time-series datasets and report real-world case studies of its utility.Later, the linkage between the 1D view (time-series chart) to the 2D view of the 2D embed-ding of time-series data, and parallel interactions such as selection and labeling, are employed to explore and examine the effectiveness of recent developments in machine learning and di-mension reduction in the context of time-series data exploration. We design a user study to evaluate and validate the effectiveness of the linkage between both a 1D and 2D visualization, and how their fitness in the context of projecting time-series data is, where different dimen-sionality reduction techniques are examined, evaluated and compared within our experimental setting.Lastly, we conclude our findings and outline possible areas for future work.

scholarly journals Discrete and Fuzzy Models of Time Series in the Tasks of Forecasting and Diagnostics

Axioms ◽  
2020 ◽  
Vol 9 (2) ◽  
pp. 49
Author(s):  
Anton Romanov ◽  
Valeria Voronina ◽  
Gleb Guskov ◽  
Irina Moshkina ◽  
Nadezhda Yarushkina
Keyword(s):  
Data Mining ◽  
Time Series ◽  
Knowledge Base ◽  
Intelligent Systems ◽  
Time Series Data ◽  
Series Data ◽  
Quality Of Data ◽  
Dynamic Data ◽  
Production Program ◽  
Time Series Data Mining

The development of the economy and the transition to industry 4.0 creates new challenges for artificial intelligence methods. Such challenges include the processing of large volumes of data, the analysis of various dynamic indicators, the discovery of complex dependencies in the accumulated data, and the forecasting of the state of processes. The main point of this study is the development of a set of analytical and prognostic methods. The methods described in this article based on fuzzy logic, statistic, and time series data mining, because data extracted from dynamic systems are initially incomplete and have a high degree of uncertainty. The ultimate goal of the study is to improve the quality of data analysis in industrial and economic systems. The advantages of the proposed methods are flexibility and orientation to the high interpretability of dynamic data. The high level of the interpretability and interoperability of dynamic data is achieved due to a combination of time series data mining and knowledge base engineering methods. The merging of a set of rules extracted from the time series and knowledge base rules allow for making a forecast in case of insufficiency of the length and nature of the time series. The proposed methods are also based on the summarization of the results of processes modeling for diagnosing technical systems, forecasting of the economic condition of enterprises, and approaches to the technological preparation of production in a multi-productive production program with the application of type 2 fuzzy sets for time series modeling. Intelligent systems based on the proposed methods demonstrate an increase in the quality and stability of their functioning. This article contains a set of experiments to approve this statement.

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