Analysis of Structural Changes in Financial Datasets Using the Breakpoint Test and the Markov Switching Model

The price movements of commodities are determined by changes in the expectations about future economic variables. Crude oil price is non-stationary, highly volatile, and unstructured in nature, which makes it very difficult to predict over short-to-medium time horizons. Some analysts have indicated that the difficulty in forecasting the crude oil price is due to the fact that economic models cannot consistently show evidence of a strong connection between commodities and economic fundamentals, and, as a result, regarded the idea that economic fundamentals help predict price values as random luck. This study aimed to overcome the limitations of the economic models through the detection of structural changes as well as breaks in the data, using a breakpoint test. The Markov switching model is used to address the price patterns that led to a different market state. The results show that there are several changes as well as breaks in the estimated model. Moreover, there is an asymmetric correlation between the crude oil price and the GDP.

An Attribute-Weighted Bayes Classifier Based on Asymmetric Correlation Coefficient

In this research, an attribute-weighted one-dependence Bayes estimation algorithm based on the asymmetric correlation coefficient is proposed. The asymmetric correlation coefficients Tau_y and Lambda_y, respectively, are used to calculate the correlation between parent attributes and category labels, then the result of calculation is regarded as weight to the parent attribute. The algorithm is applied to eight types of different datasets including binary classification and multiple classification from the UCI database. By comparing the time complexity and classification accuracy, experimental results show that the algorithm can significantly improve the classification performance with less prediction error. In addition, several baseline methods such as KNN, ANN, logistic regression and SVM are used for comparison with the proposed method.

An Item–Item Collaborative Filtering Recommender System Using Trust and Genre to Address the Cold-Start Problem

Item-based collaborative filtering is one of the most popular techniques in the recommender system to retrieve useful items for the users by finding the correlation among the items. Traditional item-based collaborative filtering works well when there exists sufficient rating data but cannot calculate similarity for new items, known as a cold-start problem. Usually, for the lack of rating data, the identification of the similarity among the cold-start items is difficult. As a result, existing techniques fail to predict accurate recommendations for cold-start items which also affects the recommender system’s performance. In this paper, two item-based similarity measures have been designed to overcome this problem by incorporating items’ genre data. An item might be uniform to other items as they might belong to more than one common genre. Thus, one of the similarity measures is defined by determining the degree of direct asymmetric correlation between items by considering their association of common genres. However, the similarity is determined between a couple of items where one of the items could be cold-start and another could be any highly rated item. Thus, the proposed similarity measure is accounted for as asymmetric by taking consideration of the item’s rating data. Another similarity measure is defined as the relative interconnection between items based on transitive inference. In addition, an enhanced prediction algorithm has been proposed so that it can calculate a better prediction for the recommendation. The proposed approach has experimented with two popular datasets that is Movielens and MovieTweets. In addition, it is found that the proposed technique performs better in comparison with the traditional techniques in a collaborative filtering recommender system. The proposed approach improved prediction accuracy for Movielens and MovieTweets approximately in terms of 3.42% & 8.58% mean absolute error, 7.25% & 3.29% precision, 7.20% & 7.55% recall, 8.76% & 5.15% f-measure and 49.3% and 16.49% mean reciprocal rank, respectively.

HUBUNGAN ANTARA KESIAPAN BELAJAR DENGAN KOMPETENSI INTI PENGETAHUAN BAHASA INDONESIA PADA SISWA KELAS V SD GUGUS LETKOL WISNU DENPASAR UTARA TAHUN PELAJARAN 2017/2018

This study was intended to determine the relationship between the readiness of learning with the core competence of Indonesian language knowledge of the V grade students of SD Gugus Letkol Wisnu North Denpasar in Academic Year 2017/2018. The type of this research was ex post facto research with asymmetric correlation. The population of this study was all students of V grade students which located at SD Gugus Letkol Wisnu North Denpasar in Academic Year 2017/2018 which has population of 340 students. The determination of the sample used proportional random sampling technique with 5% error rate and obtained many samples from the population were 172 students. The data which obtained through the results of the questionnaires about the readiness of learning and the core competence of Indonesian language knowledge were obtained from recording documents value of the end semester of the core competence of Indonesian language knowledge of the V grade students of SD Gugus Letkol Wisnu North Denpasar in semester 1. As the test was a normality test data distribution. After all the prerequisite tests were fulfilled, statistical analysis used in this research was hypothesis test which used product moment correlation analysis. Based on the results of the analysis, then rxy count = 0.818. At the significance level of 5% with n = 172, then obtained rxy table = 0,148.It could be stated that rxy count = 0,818> rxy table = 0,148, hence it can be interpreted that H0 which reads there was no significant correlation between the readiness of learning with core competence of Indonesian language knowledge of the V grade students of SD Gugus Letkol Wisnu North Denpasar rejected this mean Ha accepted. Therefore, it can be concluded that there was a significant relationship between the readiness of learning with the core competence of Indonesian language knowledge of the V grade students of SD Gugus Letkol Wisnu North Denpasar in Academic Year 2017/2018, with a positive correlation direction, it means the higher the readiness of learning so that the higher the core competence of Indonesian language knowledge.

Hydraulic conductivities identification via Ensemble Kalman Filtering with transformed data considering the risk of systematic bias

In subsurface hydrology, Ensemble Kalman Filtering (EnKF) has been coupled with groundwater flow and transport models to solve the inverse problem. Several extensions of the EnKF have been proposed to improve its performance when dealing with non-multi-Gaussian random field models of the hydraulic conductivity. One such variant is the EnKF with transformed data (tEnKF), which uses Gaussian anamorphosis within a conditioning step. Although this transformation has been used in the past to identify hydraulic conductivities, previous studies have ignored the risk of introducing a systematic bias in the spatiotemporal evolution of the hydraulic head field during the forecast steps that the update steps may not correct over time. This paper proposes that in order to evaluate the performance of tEnKFs, applications in synthetically generated random porous media should take into account this risk by incorporating prior knowledge with a multi-Gaussian conductivity correlation structure, and by adopting a reference field with asymmetric correlation structure. As an example of this application, hydraulic conductivities using the tEnKF were identified by solving a one-dimensional, single phase flow problem in a continuous random porous medium. Common concepts in Geostatistics are used to explain the hypothesis underlying both EnKF and tEnKF and to establish a clear link between the tEnKF and the stochastic simulation of conditional random fields.