3 research outputs found
Modeling and Prediction of Iran's Steel Consumption Based on Economic Activity Using Support Vector Machines
The steel industry has great impacts on the economy and the environment of
both developed and underdeveloped countries. The importance of this industry
and these impacts have led many researchers to investigate the relationship
between a country's steel consumption and its economic activity resulting in
the so-called intensity of use model. This paper investigates the validity of
the intensity of use model for the case of Iran's steel consumption and extends
this hypothesis by using the indexes of economic activity to model the steel
consumption. We use the proposed model to train support vector machines and
predict the future values for Iran's steel consumption. The paper provides
detailed correlation tests for the factors used in the model to check for their
relationships with the steel consumption. The results indicate that Iran's
steel consumption is strongly correlated with its economic activity following
the same pattern as the economy has been in the last four decades.Comment: 13 pages, 13 figure
Clustering Time-Series by a Novel Slope-Based Similarity Measure Considering Particle Swarm Optimization
Recently there has been an increase in the studies on time-series data mining
specifically time-series clustering due to the vast existence of time-series in
various domains. The large volume of data in the form of time-series makes it
necessary to employ various techniques such as clustering to understand the
data and to extract information and hidden patterns. In the field of clustering
specifically, time-series clustering, the most important aspects are the
similarity measure used and the algorithm employed to conduct the clustering.
In this paper, a new similarity measure for time-series clustering is developed
based on a combination of a simple representation of time-series, slope of each
segment of time-series, Euclidean distance and the so-called dynamic time
warping. It is proved in this paper that the proposed distance measure is
metric and thus indexing can be applied. For the task of clustering, the
Particle Swarm Optimization algorithm is employed. The proposed similarity
measure is compared to three existing measures in terms of various criteria
used for the evaluation of clustering algorithms. The results indicate that the
proposed similarity measure outperforms the rest in almost every dataset used
in this paper.Comment: 27 pages, 8 figures, 12 table
Multivariate, Multistep Forecasting, Reconstruction and Feature Selection of Ocean Waves via Recurrent and Sequence-to-Sequence Networks
This article explores the concepts of ocean wave multivariate multistep
forecasting, reconstruction and feature selection. We introduce recurrent
neural network frameworks, integrated with Bayesian hyperparameter optimization
and Elastic Net methods. We consider both short- and long-term forecasts and
reconstruction, for significant wave height and output power of the ocean
waves. Sequence-to-sequence neural networks are being developed for the first
time to reconstruct the missing characteristics of ocean waves based on
information from nearby wave sensors. Our results indicate that the Adam and
AMSGrad optimization algorithms are the most robust ones to optimize the
sequence-to-sequence network. For the case of significant wave height
reconstruction, we compare the proposed methods with alternatives on a
well-studied dataset. We show the superiority of the proposed methods
considering several error metrics. We design a new case study based on
measurement stations along the east coast of the United States and investigate
the feature selection concept. Comparisons substantiate the benefit of
utilizing Elastic Net. Moreover, case study results indicate that when the
number of features is considerable, having deeper structures improves the
performance