ALGORITHMIC METHODS FOR SEGMENTATION OF TIME SERIES: AN OVERVIEW

Adaptive and innovative application of classical data mining principles and techniques in time series analysis has resulted in development of a concept known as time series data mining. Since the time series are present in all areas of business and scientific research, attractiveness of mining of time series datasets should not be seen only in the context of the research challenges in the scientific community, but also in terms of usefulness of the research results, as a support to the process of business decision-making. A fundamental component in the mining process of time series data is time series segmentation. As a data mining research problem, segmentation is focused on the discovery of rules in movements of observed phenomena in a form of interpretable, novel, and useful temporal patterns. In this Paper, a comprehensive review of the conceptual determinations, including the elements of comparative analysis, of the most commonly used algorithms for segmentation of time series, is being considered

Exploiting Data Mining Techniques For Improving the Efficiency of Time Series Data

The research work in data mining has achieved a high attraction due to the importance of its applications This paper addresses some theoretical and practical aspects on Exploiting Data Mining Techniques for Improving the Efficiency of Time Series Data using SPSS-CLEMENTINE. This paper can be helpful for an organization or individual when choosing proper software to meet their mining needs. In this paper, we propose utilizes the famous data mining software SPSS Clementine to mine the factors that affect information from various vantage points and analyse that information. However the purpose of this paper is to review the selected software for data mining for improving efficiency of time series data. Data mining techniques is the exploration and analysis of data in order to discover useful information from huge databases. So it is used to analyse a large audit data efficiently for Improving the Efficiency of Time Series Data. SPSS- Clementine is object-oriented, extended module interface, which allows users to add their own algorithms and utilities to Clementine’s visual programming environment. The overall objective of this research is to develop high performance data mining algorithms and tools that will provide support required to analyse the massive data sets generated by various processes that is used for predicting time series data using SPSS- Clementine. The aim of this paper is to determine the feasibility and effectiveness of data mining techniques in time series data and produce solutions for this purpose

Artificial intelligence and data mining: algorithms and applications

Artificial intelligence and data mining techniques have been used in many domains to solve classification, segmentation, association, diagnosis, and prediction problems. The overall aim of this special issue is to open a discussion among researchers actively working on algorithms and applications. The issue covers a wide variety of problems for computational intelligence, machine learning, time series analysis, remote sensing image mining, and pattern recognition. After a rigorous peer review process, 20 papers have been selected from 38 submissions. The accepted papers in this issue addressed the following topics: (i) advanced artificial intelligence and data mining techniques; (ii) computational intelligence in dynamic and uncertain environments; (iii) machine learning on massive datasets; (iv) time series data analysis; (v) Spatial data mining: algorithms and applications

Using Data Mining with Time Series Data in Short-Term Stocks Prediction: A Literature Review

Data Mining (DM) methods are being increasingly used in prediction with time series data, in addition to traditional statistical approaches. This paper presents a literature review of the use of DM with time series data, focusing on short- time stocks prediction. This is an area that has been attracting a great deal of attention from researchers in the field. The main contribution of this paper is to provide an outline of the use of DM with time series data, using mainly examples related with short-term stocks prediction. This is important to a better understanding of the field. Some of the main trends and open issues will also be introduced.info:eu-repo/semantics/publishedVersio

A Review on Outlier/Anomaly Detection in Time Series Data

Recent advances in technology have brought major breakthroughs in data collection, enabling a large amount of data to be gathered over time and thus generating time series. Mining this data has become an important task for researchers and practitioners in the past few years, including the detection of outliers or anomalies that may represent errors or events of interest. This review aims to provide a structured and comprehensive state-of-the-art on outlier detection techniques in the context of time series. To this end, a taxonomy is presented based on the main aspects that characterize an outlier detection technique.KK/2019-00095 IT1244-19 TIN2016-78365-R PID2019-104966GB-I0

Research review on time series forecasting of gold price movement

This paper propose different techniques used in forecasting on time series analysis. Various techniques had been used in the field of time series forecasting from the traditional Box-Jenkins approach to the most popular neural network technique nowadays. However, there is no specifically the best method to deal with time series forecasting as the application of different time series forecasting methods has their own requirements and restrictions. In determining the movement of gold price, there are a lot of different methods being implemented by various authors to propose their models. Various time series forecasting method have been discussed in this paper which consists of several journal articles that related to gold price and some of the data mining techniques in time series forecasting retrieved from Google Scholar in this review

CNN Approaches for Time Series Classification

Time series classification is an important field in time series data-mining which have covered broad applications so far. Although it has attracted great interests during last decades, it remains a challenging task and falls short of efficiency due to the nature of its data: high dimensionality, large in data size and updating continuously. With the advent of deep learning, new methods have been developed, especially Convolutional Neural Network (CNN) models. In this paper, we present a review of our time series CNN approaches including: (i) a data-level approach based on encoding time series into frequency-domain signals via the Stockwell transform, (ii) an algorithm-level approach based on an adaptive convolutional layer filter that suits the time series in hand, and (iii) another algorithm-level approach adapted to time series classification tasks with limited annotated data, which is a global, fast and light-weight framework based on a transfer learning technique with a source learning task similar or different but related to the target learning task. These approaches are implemented on identifying human activities including normal movements of typical subjects and disorder-related movements such as stereotypical motor movements of autistic subjects. Experimental results show that our approaches improve performance of time series classification

INEFFICIENCY OF DATA MINING ALGORITHMS AND ITS ARCHITECTURE: WITH EMPHASIS TO THE SHORTCOMING OF DATA MINING ALGORITHMS ON THE OUTPUT OF THE RESEARCHES

This review paper presents a shortcoming associated to data mining algorithm(s) classification, clustering, association and regression which are highly used as a tool in different research communities. Data mining researches has successfully handling large amounts of dataset to solve the problems. An increase in data sizes was brought a bottleneck on algorithms to retrieve hidden knowledge from a large volume of datasets. On the other hand, data mining algorithm(s) has been unable to analysis the same rate of growth. Data mining algorithm(s) must be efficient and visual architecture in order to effectively extract information from huge amounts of data in many data repositories or in dynamic data streams. The increasing use of information visualization tools (architecture) and data mining algorithm(s) stems from two separate lines of research. Data visualization researchers believe in the importance of giving users an overview and insight into the data distributions. Many powerful visual graphical interfaces are built on top of statistical analysis and data mining algorithms to permit users to leverage their power without a deep understanding of the underlying technology. The combination of the graphical interface is permit to navigate through the complexity of statistical and data mining techniques to create powerful models. Therefore, there is an increasing need to understand the bottlenecks associated with the data mining algorithms in modern architectures and research community. This review paper basically to guide and help the researchers specifically to identify the shortcoming of data mining techniques with domain area in solving a certain problems they will explore. It also shows the research areas particularly a multimedia (where data can be sequential, audio signal, video signal, spatio-temporal, temporal, time series etc) in which data mining algorithms not yet used