Fast Algorithm for Outlier Detection in Time Series: Finding a Solution with a Minimum Amount of Rejected Measurement Data | Chapter 1 | Physical Science: New Insights and Developments Vol. 3

 

This study addresses the challenge of detecting coarse measurements (outliers) in time series data, a common issue in fields such as space geodynamics, geodesy, and other measurement-driven sciences. The proposed outlier detection algorithm solves two key problems: first, it finds a solution containing the maximum possible amount of measurement data remaining after detecting and removing outliers. Second, it requires the minimum possible number of arithmetic operations, estimated by the value O(NlogN), which cannot be improved in order N, to find a solution. To construct the algorithm, it was shown that the required solution should be sought in the ordered sequence of input data in the form of a set of consecutive numbers. The search for the set of maximum length is performed step by step, with the range of possible values of the set length being halved at each step.  The transition to one of the two halves is performed by checking the fulfilment of certain criteria, not for all possible values of lengths from the range under consideration, but only for its mid value. Testing the algorithm on real data obtained from laser rangefinder measurements showed the advantages of it over others, both in terms of time consumption and the amount of data remaining after detection and removal of outliers. The algorithm is robust and always finds a solution, if one exists. Its time efficiency is evident when cleaning a large amount of measurement data of outliers. It can be used for automated cleaning from outliers of observation data in information and measuring systems, in systems with artificial intelligence, as well as when solving various scientific, applied managerial and other problems using modern computer systems in order to obtain promptly the most accurate final result.

 

 

Author(s) Details

Igor V.Bezmenov
Russian Metrological Institute of Technical Physics and Radio Engineering, Mendeleevo, Moscow Region, Russia.

 

Please see the link:- https://doi.org/10.9734/bpi/psniad/v3/6424

 

Trend Detection in Time Series Using the Minimizing Sets Method: Applications in Space Geodynamics and Beyond | Chapter 8 | Physical Science: New Insights and Developments Vol. 1

A significant problem in achieving the required level of accuracy of modern computing complexes and systems is the detection of coarse measurements (outliers) in the time series of data at the preprocessing stage. The proper detection and removal of outliers from measurement data is a necessary step in any scientific application to obtain the most accurate final result. This chapter addresses the challenge of detecting trends in time series data contaminated with outliers, a common issue in fields such as space geodynamics, geodesy, and other measurement-driven sciences. The proposed solution builds on the author’s previously developed Minimising Sets (MS) method, which iteratively constructs a trend by maximising the usable portion of the data and minimising the influence of outliers. The method is extended beyond power polynomials to include trigonometric functions with fixed frequencies and harmonic functions with unknown parameters, increasing its applicability across diverse physical systems. Simulations demonstrate that the approach yields accurate trend approximations and robust outlier detection, without relying on arbitrary thresholds. The technique shows promise for high-precision scientific applications requiring reliable preprocessing of noisy measurement data.

 

Author(s) Details

Igor V. Bezmenov
Russian Metrological Institute of Technical Physics and Radio Engineering, Mendeleevo, Moscow Region, Russia.

 

Please see the book here:- https://doi.org/10.9734/bpi/psniad/v1/5621

On Optimality of Entropy Like Functional in Terms of Distance Function | Chapter 2 | Research Highlights in Mathematics and Computer Science Vol. 7

 The main objective concerning this investigation is to produce method as optimal resolutions of minimization problem in agreements of distance function or directed difference. The special feature of this approach search out define the connection between distance functions.

Author(s) Details:

Rohit Kumar Verma,
Department of Mathematics, Bharti Vishwavidyalaya, Durg, C.G., India.

Please see the link here: https://stm.bookpi.org/RHMCS-V7/article/view/9996

The Out-of-Kilter Algorithm and Its Network Flow Implementations | Chapter 7 | Theory and Practice of Mathematics and Computer Science Vol. 10

 D.R. Fulkerson [1] published the out-of-kilter algorithm, which computes the solution to the minimum-cost flow issue in a flow network. The Out-of-Kilter technique can be used to solve problems like the transportation problem, which can be described as a maximum flow and minimal cost – maximum flow problem. To begin, the algorithm creates an initial flow along the arcs and assigns a number to each of the network's nodes. See, for example, Bazaraa, which employs Complementary Slackness Optimality Conditions (CSOC). The method developed by Jarvis and Sherali [2] looks for out-of-kilter arcs (those that do not satisfy CSOC conditions). If there aren't any, the algorithm is finished. The flow must be increased or decreased to bring arcs that do not satisfy the CSOC theorem into alignment. According to the demand, the algorithm will hunt for a path that either enhances or decreases the flow. This is repeated until all of the arcs are aligned, at which time the procedure is finished. There is no conceivable flow if no paths to improve the system are found. The Out-of-Kilter algorithm is used to determine the best solution to any network flow problem. This comprises transit, assignment, and shortest path problems, among others. Computer solutions are presented using a Pascal programme and Matlab.

Author(s) Details

W. H. Moolman
Department of Mathematical Sciences and Computing, Walter Sisulu University, Mthatha, South Africa.

View Book :- https://stm.bookpi.org/TPMCS-V10/article/view/1077

Current Research on Significance of Artificial Intelligence and Machine Learning Techniques in Smart Cloud Computing: A Review | Chapter 3 | Recent Studies in Mathematics and Computer Science Vol. 2

Realization of the tremendous features and facilities provided by Cloud Computing by the geniuses in the world of digital marketing increases its demand. As customer satisfaction is the manifest of this ever shining field, balancing its load becomes a major issue. Various heuristic and meta-heuristic algorithms were applied to get optimum solutions. The current era is much attracted with the provisioning of self-manageable, self-learnable, self-healable, and self-configurable smart systems. To get self-manageable Smart Cloud, various Artificial Intelligence and Machine Learning (AI-ML) techniques and algorithms are revived. In this review, recent trend in the utilization of AI-ML techniques, their applied areas, purpose, their merits and demerits are highlighted. These techniques are further categorized as instance-based machine learning algorithms and reinforcement learning techniques based on their ability of learning. Reinforcement learning is preferred when there is no training data set.  It leads the system to learn by its own experience itself even in dynamic environment.

Author(s)  Details

V. Radhamani 
Department of Computer Science, Hindusthan College of Arts and Science, Coimbatore, Tamilnadu, India.

G. Dalin
Department of Computer Science, Hindusthan College of Arts and Science, Coimbatore, Tamilnadu, India.

View Book :- http://bp.bookpi.org/index.php/bpi/catalog/book/182