Latent Network Construction for Univariate Time Series Based on Variational Auto-Encode

Time series analysis has been an important branch of information processing, and the conversion of time series into complex networks provides a new means to understand and analyze time series. In this work, using Variational Auto-Encode (VAE), we explored the construction of latent networks for univariate time series. We first trained the VAE to obtain the space of latent probability distributions of the time series and then decomposed the multivariate Gaussian distribution into multiple univariate Gaussian distributions. By measuring the distance between univariate Gaussian distributions on a statistical manifold, the latent network construction was finally achieved. The experimental results show that the latent network can effectively retain the original information of the time series and provide a new data structure for the downstream tasks.

Comparative Analysis of Non-Linear GNSS Geodetic Time Series Filtering Techniques: El Hierro Volcanic Process (2010–2014)

GNSS geodetic time series analysis allows the study of the geodynamic behavior of a specific terrestrial area. These time series define the temporal evolution of the geocentric or topocentric coordinates obtained from geodetic stations, which are linear or non-linear depending, respectively, on the tectonic or volcanic–tectonic character of a region. Linear series are easily modeled but, for the study of nonlinear series, it is necessary to apply filtering techniques that provide a more detailed analysis of their behavior. In this work, a comparative analysis is carried out between different filtering techniques and non–linear GNSS time series analysis: 1sigma–2sigma filter, outlier filter, wavelet analysis, Kalman filter and CATS analysis (Create and Analyze Time Series). This comparative methodology is applied to the time series that describe the volcanic process of El Hierro island (2010–2014). Among them, the time series of the slope distance variation between FRON (El Hierro island) and LPAL (La Palma island) stations is studied, detecting and analyzing the different phases involved in the process.

Globally observed trends in mean and extreme river flow attributed to climate change

Anthropogenic climate change is expected to affect global river flow. Here, we analyze time series of low, mean, and high river flows from 7250 observatories around the world covering the years 1971 to 2010. We identify spatially complex trend patterns, where some regions are drying and others are wetting consistently across low, mean, and high flows. Trends computed from state-of-the-art model simulations are consistent with the observations only if radiative forcing that accounts for anthropogenic climate change is considered. Simulated effects of water and land management do not suffice to reproduce the observed trend pattern. Thus, the analysis provides clear evidence for the role of externally forced climate change as a causal driver of recent trends in mean and extreme river flow at the global scale.

Multiple mechanisms drive genomic adaptation to extreme O2 levels in Drosophila melanogaster

To detect the genomic mechanisms underlying evolutionary dynamics of adaptation in sexually reproducing organisms, we analyze multigenerational whole genome sequences of Drosophila melanogaster adapting to extreme O2 conditions over an experiment conducted for nearly two decades. We develop methods to analyze time-series genomics data and predict adaptive mechanisms. Here, we report a remarkable level of synchronicity in both hard and soft selective sweeps in replicate populations as well as the arrival of favorable de novo mutations that constitute a few asynchronized sweeps. We additionally make direct experimental observations of rare recombination events that combine multiple alleles on to a single, better-adapted haplotype. Based on the analyses of the genes in genomic intervals, we provide a deeper insight into the mechanisms of genome adaptation that allow complex organisms to survive harsh environments.

A Novel MCDM Method Based on Plithogenic Hypersoft Sets under Fuzzy Neutrosophic Environment

In this paper, we advance the study of plithogenic hypersoft set (PHSS). We present four classifications of PHSS that are based on the number of attributes chosen for application and the nature of alternatives or that of attribute value degree of appurtenance. These four PHSS classifications cover most of the fuzzy and neutrosophic cases that can have neutrosophic applications in symmetry. We also make explanations with an illustrative example for demonstrating these four classifications. We then propose a novel multi-criteria decision making (MCDM) method that is based on PHSS, as an extension of the technique for order preference by similarity to an ideal solution (TOPSIS). A number of real MCDM problems are complicated with uncertainty that require each selection criteria or attribute to be further subdivided into attribute values and all alternatives to be evaluated separately against each attribute value. The proposed PHSS-based TOPSIS can be used in order to solve these real MCDM problems that are precisely modeled by the concept of PHSS, in which each attribute value has a neutrosophic degree of appurtenance corresponding to each alternative under consideration, in the light of some given criteria. For a real application, a parking spot choice problem is solved by the proposed PHSS-based TOPSIS under fuzzy neutrosophic environment and it is validated by considering two different sets of alternatives along with a comparison with fuzzy TOPSIS in each case. The results are highly encouraging and a MATLAB code of the algorithm of PHSS-based TOPSIS is also complied in order to extend the scope of the work to analyze time series and in developing algorithms for graph theory, machine learning, pattern recognition, and artificial intelligence.

Multiscale Fractional Cumulative Residual Entropy of Higher-Order Moments for Estimating Uncertainty

The fractional cumulative residual entropy is not only a powerful tool for the analysis of complex system, but also a promising way to analyze time series. In this paper, we present an approach to measure the uncertainty of non-stationary time series named higher-order multiscale fractional cumulative residual entropy. We describe how fractional cumulative residual entropy may be calculated based on second-order, third-order, fourth-order statistical moments and multiscale method. The implementation of higher-order multiscale fractional cumulative residual entropy is illustrated with simulated time series generated by uniform distribution on [0, 1]. Finally, we present the application of higher-order multiscale fractional cumulative residual entropy in logistic map time series and stock markets time series, respectively.

ANALISIS FAKTOR-FAKTOR YANG MEMPENGARUHI VOLUME EKSPOR BIJI KAKAO INDONESIA KE MALAYSIA

This study aims to analyze the factors that affect the volume of Indonesian cocoa exports to Malaysia. Multiple linear regression and ordinary least squares (OLS) were employed to analyze time series of data from 2005 until 2013. Based on the analysis, it is obtained that factors that significantly effect the volume of Indonesian cocoa exports to Malaysia with a significance level (α) five percent are the real prices of Indonesian cocoa exports to Malaysia and the real prices of cocoa beans the international market.

ANALISIS FAKTOR-FAKTOR YANG MEMPENGARUHI VOLUME EKSPOR BIJI KAKAO INDONESIA KE MALAYSIA

This study aims to analyze the factors that affect the volume of Indonesian cocoa exports to Malaysia. Multiple linear regression and ordinary least squares (OLS) were employed to analyze time series of data from 2005 until 2013. Based on the analysis, it is obtained that factors that significantly effect the volume of Indonesian cocoa exports to Malaysia with a significance level (α) five percent are the real prices of Indonesian cocoa exports to Malaysia and the real prices of cocoa beans the international market.

Peramalan Banyaknya Pengunjung Pantai Glagah Menggunakan Metode Autoregressive Integrated Moving Average Exogenous (ARIMAX) dengan Efek Variasi Kalender

<pre>Glagah Beach is one of the tourist destinations in Kulon Progo Regency, Yogyakarta which is the most visited by tourists. Glagah Beach visitors data show that in the month of Eid Al-Fitr there was a significant increase. This shows that there is an effect of the calendar variation of Eid al-Fitr. Therefore, it is needed a method that can be used to analyze time series data which contains effects of calendar variations, that is ARIMAX method. The aim of this study are to find the best ARIMAX model and to predict the number of visitors to Glagah Beach in the future. The result shows that the best ARIMAX model was ARIMAX([24],0,0). Forecasting from January to September 2016 are 37211, 21306, 26247, 24148, 28402, 29309, 81724, 26029, and 23688 visitors.</pre><br /> Keywords: Glagah Beach; variation of calendar; Eid al-Fitr; ARIMAX.