Models and methods for prediction problem of evolving graphs

Neural Network Techniques for Time Series Prediction: A Review

JOIV International Journal on Informatics Visualization ◽

10.30630/joiv.3.3.281 ◽

2019 ◽

Vol 3 (3) ◽

Author(s):

Muhammad Faheem Mushtaq ◽

Urooj Akram ◽

Muhammad Aamir ◽

Haseeb Ali ◽

Muhammad Zulqarnain

Keyword(s):

Neural Network ◽

Neural Networks ◽

Time Series ◽

Time Series Data ◽

Weather Prediction ◽

Time Series Prediction ◽

Series Data ◽

Prediction Problem ◽

Neural Network Models ◽

Physical Time

It is important to predict a time series because many problems that are related to prediction such as health prediction problem, climate change prediction problem and weather prediction problem include a time component. To solve the time series prediction problem various techniques have been developed over many years to enhance the accuracy of forecasting. This paper presents a review of the prediction of physical time series applications using the neural network models. Neural Networks (NN) have appeared as an effective tool for forecasting of time series. Moreover, to resolve the problems related to time series data, there is a need of network with single layer trainable weights that is Higher Order Neural Network (HONN) which can perform nonlinearity mapping of input-output. So, the developers are focusing on HONN that has been recently considered to develop the input representation spaces broadly. The HONN model has the ability of functional mapping which determined through some time series problems and it shows the more benefits as compared to conventional Artificial Neural Networks (ANN). The goal of this research is to present the reader awareness about HONN for physical time series prediction, to highlight some benefits and challenges using HONN.

Download Full-text

Flow shop failure prediction problem based on Grey-Markov model

Personal and Ubiquitous Computing ◽

10.1007/s00779-021-01618-0 ◽

2021 ◽

Author(s):

Kai Guo ◽

Jiyao Zhao ◽

Yan Liang

Keyword(s):

Markov Model ◽

Flow Shop ◽

Failure Prediction ◽

Prediction Problem

Download Full-text

Shall I Work with Them? A Knowledge Graph-Based Approach for Predicting Future Research Collaborations

Entropy ◽

10.3390/e23060664 ◽

2021 ◽

Vol 23 (6) ◽

pp. 664

Author(s):

Nikos Kanakaris ◽

Nikolaos Giarelis ◽

Ilias Siachos ◽

Nikos Karacapilidis

Keyword(s):

Language Processing ◽

Scientific Knowledge ◽

Link Prediction ◽

Performance Metrics ◽

Future Research ◽

Knowledge Graph ◽

Prediction Problem ◽

Textual Information ◽

Research Collaborations ◽

Processing Techniques

We consider the prediction of future research collaborations as a link prediction problem applied on a scientific knowledge graph. To the best of our knowledge, this is the first work on the prediction of future research collaborations that combines structural and textual information of a scientific knowledge graph through a purposeful integration of graph algorithms and natural language processing techniques. Our work: (i) investigates whether the integration of unstructured textual data into a single knowledge graph affects the performance of a link prediction model, (ii) studies the effect of previously proposed graph kernels based approaches on the performance of an ML model, as far as the link prediction problem is concerned, and (iii) proposes a three-phase pipeline that enables the exploitation of structural and textual information, as well as of pre-trained word embeddings. We benchmark the proposed approach against classical link prediction algorithms using accuracy, recall, and precision as our performance metrics. Finally, we empirically test our approach through various feature combinations with respect to the link prediction problem. Our experimentations with the new COVID-19 Open Research Dataset demonstrate a significant improvement of the abovementioned performance metrics in the prediction of future research collaborations.

Download Full-text