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.

A Mathematical Database to Process Time Series

In scientific research, the results of an experiment commonly take the form of a time series, in which such time series consists of measurements collected from a sensor over time. After time series are stored, mathematical models are derived using numerical methods. Even though there exist plenty of tools to store and analyze time series data, there is scarce research aimed at storing and querying derived models, which are the most important mechanism for a scientist to understand data. In this article, the authors propose to help scientists with a flexible database structure to persist and manage mathematical models with a mathematical models store, with extended features, to handle time series. In this article, the authors introduce the concept of a mathematical models store enriched with numerical processing methods to allow queries based on raw time series data. Then they introduce a prototype, that is an implementation of such a data store with PostgreSQL.

Stochastic methods for inferring states of cell migration

Cell migration refers to the ability of cells to translocate across a substrate or through a matrix. To achieve net movement requires spatiotemporal regulation of the actin cytoskeleton. Computational approaches are neceary to identify and quantify the regulatory mechanisms that generate directed cell movement. To address this need, we developed computational tools, based on stochastic modeling, to analyze time series data for the position of randomly migrating cells. Our approach allows parameters that characterize cell movement to be efficiently estimated from time series data. We applied our methods to analyze the random migration of Mouse Embryonic Fibroblasts (MEFS). Our analysis revealed that these cells exist in two distinct states of migration characterized by differences in cell speed and persistence. Further analysis revealed that the Rho-family GTPase RhoG plays a role in establishing these two states. An important feature of our computational approach is that it provides a method for predicting the current migration state of an individual cell from time series data. Using this feature, we demonstrate that HeLa cells also exhibit two states of migration, and that these states correlate with differences in the spatial distribution of active Rac1.

FAKTOR-FAKTOR YANG MEMPENGARUHIKURS RUPIAH TERHADAP YEN TAHUN 1970 - 2002: ERROR CORRECTION MODEL (ECM)

This research aims to find out factors influencing the exchange rate of rupiah toward yen. The approach used to analyze time series data in this study is monetary approach with ECM as the chosen regression model. The year of observation was begun in 1970-2002. Based on regression which done, the result showed that there is the significant correlation between independent variable (MI,Yreal, NP1) with dependent variable (exchange rate of Rupiah fYen). The correlation happens either in long or short term.

WAVELET ANALYSIS OF TIME-SERIES: COHERENT STRUCTURES, CHAOS AND NOISE

The continuous and orthogonal wavelet transforms are used to analyze time-series data. The analysis involves signal decomposition into scale components using both Grossman–Morlet and Daubechies type wavelets. A number of simulated and experimental data vectors exhibiting different types of coherent structures, chaos and noise is analyzed. The study shows that wavelet analysis provides a unifying framework for the description of many phenomena in time-series.

Stability and Change in European Electorates

Arguing counter to the accepted positions of political sociology, we contend that voters' decisions are best explained by the absence or presence of strong loyalties to political parties rather than by social or economic factors. Hence, in areas where most people have strong party attachments, marked change in the partisan division of the vote occurs only when an exceptionally large number of new voters enters the electoral arena; alterations in the social composition of a party's voters follow changes in the occupation or social categories of those who consistently vote for the party. In presenting this argument, we analyze time-series data for Britain, West Germany, and Sweden which negate the predicted development of “catch-all” electorates, and we test the relative power of party and class variables as predictors of voting behavior in Butler's and Stokes's panel study of British voters between 1963 and 1970.