The Science Of Data Mining

3544 Words15 Pages
ABSTRACT

Many real life applications require the ability to decide whether a new set of observation is similar to the same distribution over a time series or not. It is considered for many application domains as a milestone and a watershed to their decision making process. Business and research sectors such as medical, financial, IT, cyber security and even crime investigation and terrorism are interested to invest in this field to have the ability for real time detection of unusual behavior.
We are living in an era were we have zillions of data streams that need to be captured, analysed and studied to have more knowledge on different aspects of life and their effect on each other. These data streams are collected and recorded over
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Real time anomaly detection in streaming data is something valuable in many domains, especially in environments where there are sensors that produce data streams changing over time. There are various existing anomaly detection techniques that are developed and experimented across different industries.. The motivation for partitioning time series into similar motifs is to give better understanding of the data characteristics.
In this study we will provide state- of-the-art review in the area of anomaly detection based on non-parametric techniques and will assess different existing techniques and introduce a novel methodology for anomaly detection using dynamic evolving subsequence clustering.

INTRODUCTION

Time series is a very important factor in business today. Organizations always depend on forecasting methods for their management decisions. The methodology itself depends on the availably of the required data and accordingly a judgmental or statistical approach is chosen. Almost every functional area of the organization makes use of the forecasting, for example financial experts use forecasting for cash flow analysis, stock price fluctuations and companies’ valuations. Also personnel departments depend on forecast for their recruitment plans. Logistics and supply chain forecast their inventory levels and their supply and demand. Moreover, there is a huge demand to utilize time series data in
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