scholarly journals PENDEKATAN REGRESI NONPARAMETRIK DENGAN MENGGUNAKAN ESTIMATOR KERNEL PADA DATA KURS RUPIAH TERHADAP DOLAR AMERIKA SERIKAT

2018 ◽  
Vol 7 (4) ◽  
pp. 305 ◽  
Author(s):  
DEWA AYU DWI ASTUTI ◽  
I GUSTI AYU MADE SRINADI ◽  
MADE SUSILAWATI
Keyword(s):  
Time Series ◽  
Cross Validation ◽  
Time Series Data ◽  
Kernel Estimator ◽  
Kernel Functions ◽  
Series Data ◽  
Smoothing Technique ◽  
Data Types ◽  
Optimal Bandwidth ◽  
Us Dollar

Nonparametric regression can be applied for some data types one of them is time series data. The technique of this method is called smoothing technique. There are several smoothing techniques however this study used kernel estimator with seven kernel functions in data of rupiah exchange rate to US dollar. The analysis with R shows that by using minimum Generalized Cross Validation (GCV) criteria, seven functions produce various optimal bandwidth value but has similar curves estimation. The conclusion is that by using kernel estimator in time series data support that choosing the optimal bandwidth is more important than choosing the kernel functions.

scholarly journals ANALISIS FAKTOR-FAKTOR YANG MEMPENGARUHI IMPOR BERAS DI INDONESIA

2019 ◽  
Vol 11 (2) ◽  
pp. 183-201
Author(s):  
Yona Namira ◽  
Iskandar Andi Nuhung ◽  
Mudatsir Najamuddin
Keyword(s):  
Time Series ◽  
Linear Regression ◽  
Multiple Linear Regression ◽  
Time Series Data ◽  
Series Data ◽  
Test Results ◽  
The Us ◽  
Rice Prices ◽  
Us Dollar ◽  
Spss Software

This study aims to 1) identify factors that affect the import of rice in Indonesia 2) analyze the influence of these factors on imports of rice in Indonesia. The data used in this research are time series data from 1994 to 2013 from the Central Statistics Agency (BPS), the Ministry of Agriculture, Ministry of Commerce, National Logistics Agency (Bulog), and Bank Indonesia. Multiple linear regression through SPSS software version 21 was employed to analyze the data. The test results together indicated the variables of productions, consumptions, stocks of rice, domestic rice prices, international rice prices and the rupiah against the US dollar affect the imports of rice in Indonesia.

scholarly journals A novel cross-validation strategy for artificial neural networks using distributed-lag environmental factors

PLoS ONE ◽  
2021 ◽  
Vol 16 (1) ◽  
pp. e0244094
Author(s):  
Chao-Yu Guo ◽  
Tse-Wei Liu ◽  
Yi-Hau Chen
Keyword(s):  
Time Series ◽  
Cross Validation ◽  
Time Series Data ◽  
Series Data ◽  
Prediction Errors ◽  
Statistical Regression ◽  
Ann Model ◽  
Distributed Lag ◽  
Minimum Number ◽  
Artificial Neural

In recent years, machine learning methods have been applied to various prediction scenarios in time-series data. However, some processing procedures such as cross-validation (CV) that rearrange the order of the longitudinal data might ruin the seriality and lead to a potentially biased outcome. Regarding this issue, a recent study investigated how different types of CV methods influence the predictive errors in conventional time-series data. Here, we examine a more complex distributed lag nonlinear model (DLNM), which has been widely used to assess the cumulative impacts of past exposures on the current health outcome. This research extends the DLNM into an artificial neural network (ANN) and investigates how the ANN model reacts to various CV schemes that result in different predictive biases. We also propose a newly designed permutation ratio to evaluate the performance of the CV in the ANN. This ratio mimics the concept of the R-square in conventional statistical regression models. The results show that as the complexity of the ANN increases, the predicted outcome becomes more stable, and the bias shows a decreasing trend. Among the different settings of hyperparameters, the novel strategy, Leave One Block Out Cross-Validation (LOBO-CV), demonstrated much better results, and the lowest mean square error was observed. The hyperparameters of the ANN trained by the LOBO-CV yielded the minimum number of prediction errors. The newly proposed permutation ratio indicates that LOBO-CV can contribute up to 34% of the prediction accuracy.

scholarly journals Novelty detection for time series data analysis in water distribution systems using support vector machines

2010 ◽  
Vol 13 (4) ◽  
pp. 672-686 ◽  
Author(s):  
Stephen R. Mounce ◽  
Richard B. Mounce ◽  
Joby B. Boxall
Keyword(s):  
Time Series ◽  
Anomaly Detection ◽  
Support Vector Regression ◽  
Distribution Systems ◽  
Water Distribution ◽  
Time Series Data ◽  
Water Distribution Systems ◽  
Kernel Functions ◽  
Series Data ◽  
Support Vector

The sampling frequency and quantity of time series data collected from water distribution systems has been increasing in recent years, giving rise to the potential for improving system knowledge if suitable automated techniques can be applied, in particular, machine learning. Novelty (or anomaly) detection refers to the automatic identification of novel or abnormal patterns embedded in large amounts of “normal” data. When dealing with time series data (transformed into vectors), this means abnormal events embedded amongst many normal time series points. The support vector machine is a data-driven statistical technique that has been developed as a tool for classification and regression. The key features include statistical robustness with respect to non-Gaussian errors and outliers, the selection of the decision boundary in a principled way, and the introduction of nonlinearity in the feature space without explicitly requiring a nonlinear algorithm by means of kernel functions. In this research, support vector regression is used as a learning method for anomaly detection from water flow and pressure time series data. No use is made of past event histories collected through other information sources. The support vector regression methodology, whose robustness derives from the training error function, is applied to a case study.

scholarly journals Pemodelan Data Time Series dengan Pendekatan Regresi Nonparametrik B-Spline

AKSIOMA ◽  
2020 ◽  
Vol 11 (1) ◽  
pp. 9-16
Author(s):  
Zulaiha Rahasia ◽  
Resmawan Resmawan ◽  
Dewi Rahmawaty Isa
Keyword(s):  
Time Series ◽  
Cross Validation ◽  
Time Series Data ◽  
Series Data ◽  
B Spline ◽  
Generalized Cross Validation ◽  
Nonparametric Approach ◽  
Currency Exchange ◽  
Currency Exchange Rates ◽  
The Us

Spline is one of the nonparametric approach, to adjust data so the final model has good flexibility. The purpose of this research is to model the time series data in the form of currency exchange rates by using the nonparametric B-spline approach. In B-spline modelling, determination of the order for the model, and the number and the placement of the knot are the criteria that must be considered. The best B-spline model obtained based on the selection of the optimal knot points with minimum Generalized Cross Validation (GCV) criteria. The modelling in this research use data on the exchange rate of the rupiah toward the US dollar in the period January 2014 - December 2018. The best B-spline model obtained by the 2 point knot approach, at points 11935.10 and 12438.29, with GCV valueequals to 55683.09.Keywords: Nonparametric Regression; B-Spline; Generalized Cross Validation

scholarly journals ANALIZING FACTOR THAT AFFECTING IMPORT OF MAIZE IN INDONESIA ANALISIS FAKTOR-FAKTOR YANG MEMPENGARUHI IMPOR JAGUNG DI INDONESIA

2019 ◽  
Vol 12 (2) ◽  
Author(s):  
Raditya Audayuda ◽  
Elpawati E ◽  
Iwan Aminudin
Keyword(s):  
Time Series ◽  
Regression Analysis ◽  
Linear Regression ◽  
Time Series Data ◽  
Linear Regression Analysis ◽  
Series Data ◽  
F Test ◽  
Maize Production ◽  
Us Dollar

The purpose of The study is to identify the factor that affecting import of maize in Indonesia, Ana to analyze The factor that affecting import of maize in Indonesia. The data used in The Study is Time series data from 1990 to 2014 sourced from BPS(Badan Pusat Statistik) and Kementrian Pertanian. The method used in The Study is linear regression analysis using SPSS 18 software. Statistics Test that used in this Study including R2 , F-test, and T-test.In this Study we can concluded that R2 test value is 0,703 that means 70,3% import of maize explained by variable that used in model, which is: maize production (produksi jagung), maize consumption (konsumsi jagung), maize domestic prize (harga jagung domestik), maize import prize (harga jagung impor), and Rupiah to US Dollar currency (Nilai tukar rupiah terhadap dollar Amerika), and remaining 29,7% remains explained by another variable that exclude by this model. After all The testing, results shows all variable in The model affecting maize import simultaneously, and partial Test shows maize production, maize consumption, maize domestic prize, and Rupiah to US Dollar currency partially affecting import of maize in Indonesia, and maize import prize variable didn’t affect import of maize in Indonesia.

Parameter Selection of SVR Based on Improved K-Fold Cross Validation

2013 ◽  
Vol 462-463 ◽  
pp. 182-186 ◽  
Author(s):  
Ju E Wang ◽  
Jian Zhong Qiao
Keyword(s):  
Time Series ◽  
Cross Validation ◽  
Time Series Data ◽  
Parameter Selection ◽  
Small Samples ◽  
Series Data ◽  
Support Vector ◽  
Model Parameters ◽  
Fold Cross Validation ◽  
Selection Of

This article firstly uses svm to forecast cashmere price time series. The forecasting result mainly depends on parameter selection. The normal parameter selection is based on k-fold cross validation. The k-fold cross validation is suitable for classification. In this essay, k-fold cross validation is improved to ensure that only the older data can be used to forecast latter data to improve prediction accuracy. This essay trains the cashmere price time series data to build mathematical model based on SVM. The selection of the model parameters are based on improved cross validation. The price of Cashmere can be forecasted by the model. The simulation results show that support vector machine has higher fitting precision in the situation of small samples. It is feasible to forecast cashmere price based on SVM.

scholarly journals EFEKTIVITAS PAJAK BUMI & BANGUNAN PERDESAAN DAN PERKOTAAN TERHADAP PENDAPATAN ASLI DAERAH (PAD) DI KABUPATEN TASIKMALAYA

2019 ◽  
Vol 3 (2) ◽  
pp. 133-140
Author(s):  
Taufik Wibisono ◽  
Yani Sri Mulyani
Keyword(s):  
Time Series ◽  
Time Series Data ◽  
Secondary Data ◽  
Government Funding ◽  
Series Data ◽  
Tax Revenue ◽  
Data Types ◽  
Regional Autonomy ◽  
Rural And Urban ◽  
The Government

ABSTRACTTHE EFFECTIVENESS OF EARTH TAXES & RURAL AND URBAN BUILDING TO REGIONAL ORIGINAL INCOME (PAD)IN TASIKMALAYA DISTRICT. In the current era of regional autonomy the government needs substantial funds to meet government funding in implementing regional development through Regional Original Revenue (PAD), which is a source of regional revenue. The purpose of reseach was to determine the level of effectiveness and contribution of Regional Taxes to PAD.The data sources used in this research were  secondary data with Time Series data types. Secondary data used was the Budget Realization Report for Tasikmalaya Regency Regional Tax Revenue in 2018.The effectiveness of the land and building tax on regional original income was in the effective category, in other words that the level of effectiveness of land and building tax is at 97%.The contribution of land and building tax to regional original income was in the category 111,8% in other words that the level of land and building tax contributions was  in the numbers 35,43% Keywords: effectiveness. Earth and building tax, income  Abstrak Dalam era otonomi daerah saat ini pemerintah membutuhkan dana yang cukup besar untuk memenuhi pembiayaan pemerintah dalam melaksanakan pembangunan daerah melalui Pendapatan Asli Daerah (PAD) yang merupakan sumber penerimaan daerah. Tujuan penelitian ini adalah untuk mengetahui tingkat efektivitas dan kontribusi Pajak Daerah terhadap PAD.Sumber data yang digunakan dalam penelitian ini adalah data sekunder dengan jenis data Time Series. Data sekunder yang digunakan yaitu Laporan Realisasi Anggaran Penerimaan Pajak Daerah Kabupeten Tasikmalaya tahun 2018. Efektivitas pajak bumi dan bangunan daerah terhadap pendapatan asli daerah berada pada kategori efektif, dengan kata lain bahwa tingkat efektifitas pajak bumi dan bangunan berada di angka 111,8 %. Kontribusi pajak bumi dan bangunan terhadap pendapatan asli daerah berada dalam katergori cukup baik dengan kata lain bahwa tingkat kontribusi pajak bumi dan bangunan berada pada angka 35,43%. Kata kunci: efektivitas.pajak bumi dan bangunan, pendapatan   

scholarly journals Prevention of Unintended Appearance in Photos Based on Human Behaviors Analysis

2020 ◽  
Author(s):  
Yuhi Kaihoko ◽  
Phan Xuan Tan ◽  
Eiji Kamioka
Keyword(s):  
Time Series ◽  
Cross Validation ◽  
Time Series Data ◽  
Video Data ◽  
Network Services ◽  
Series Data ◽  
Detection Accuracy ◽  
Human Behaviors ◽  
Wearable Camera ◽  
Social Network Services

Many people can take photos with smartphones and easily post photos via SNS (Social Network Services). This has caused a social problem that unintended appearance in photos may threaten the privacy of photographed persons. For this issue, numerous studies have already been introduced to prevent the unintended appearance in photos from the photographer’s side, but only a few methods tackled this from the photographed person's side. Therefore, we considered calling attention to a situation that a photo-taking behavior by a photographer can be automatically detected by using a wearable camera worn by a photographed person. In this paper, we propose an approach to detect photo-taking behaviors in video data taken from the wearable camera, analyzing specific human skeleton information. OpenPose is utilized to obtain the human’s skeleton information and the time-series data are analyzed. In addition, we compare two similar behaviors which are photo-taking behaviors and net-surfing behaviors. These video data are distinguished by DP matching and cross-validation. Finally, it is concluded that the detection accuracy of photo-taking behaviors is about 92.5%, which is satisfactory enough for this research purpose.

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