scholarly journals Prediksi Harga Beras Sultan dan Membramo di Kota Manado dengan Menggunakan Model ARIMA

Jurnal MIPA ◽  
2013 ◽  
Vol 2 (1) ◽  
pp. 1
Author(s):  
Varra Wuwung ◽  
Nelson Nainggolan ◽  
Marline Paendong
Keyword(s):  
Time Series ◽  
P Value ◽  
Arima Models ◽  
Result Show ◽  
Normally Distributed

Pada makalah ini diuraikan model ARIMA dari harga beras di kota Manado yang meliputi beras Sultan dan beras Membramo. Data yang diamati adalah data bulanan dari Januari 2007 sampai dengan Maret 2012. Hasil analisis time series menunjukan bahwa untuk beras Sultan diperoleh model ARIMA(1,1,1) dan beras Membramo diperoleh model ARIMA(1,1,0). Hasil diagnosis menunjukan bahwa galat dari model untuk beras Sultan dan beras Membramo sudah berdistribusi normal dengan p-value lebih dari 0,05 yaitu masing-masing 0,15 dan 0,07. Prediksi harga beras untuk tiga periode kedepan untuk beras Sultan berkisar antara Rp. 8.287 sampai Rp. 8.389, dan beras Membramo berkisar antara Rp. 8.482 sampai Rp. 8.593.This paper described ARIMA models of the rice prince in Manado, that is sultan and membramo rice. The observed data is monthly from January-2007 to March-2012. The result show that the models for sultan is ARIMA(1,1,1) and for membramo is ARIMA(1,1,0). The diagnosis results show that the residuals of the models for sultan and membramo is normally distributed with a p-value more than 0,05, that is 0,15 and 0,07 respectively. The prediction of price for the next three periods for sultan from Rp. 8.287,30 to Rp. 8.389,92 and for membramo from Rp. 8.482 to Rp. 8.593.

scholarly journals Hybrid Model for Time Series of Complex Structure with ARIMA Components

Mathematics ◽  
2021 ◽  
Vol 9 (10) ◽  
pp. 1122
Author(s):  
Oksana Mandrikova ◽  
Nadezhda Fetisova ◽  
Yuriy Polozov
Keyword(s):  
Neural Network ◽  
Time Series ◽  
Hybrid Model ◽  
Complex Structure ◽  
Arima Model ◽  
Ionospheric Parameter ◽  
Arima Models ◽  
Complicated Structure ◽  
Threshold Functions ◽  
Anomalous Component

A hybrid model for the time series of complex structure (HMTS) was proposed. It is based on the combination of function expansions in a wavelet series with ARIMA models. HMTS has regular and anomalous components. The time series components, obtained after expansion, have a simpler structure that makes it possible to identify the ARIMA model if the components are stationary. This allows us to obtain a more accurate ARIMA model for a time series of complicated structure and to extend the area for application. To identify the HMTS anomalous component, threshold functions are applied. This paper describes a technique to identify HMTS and proposes operations to detect anomalies. With the example of an ionospheric parameter time series, we show the HMTS efficiency, describe the results and their application in detecting ionospheric anomalies. The HMTS was compared with the nonlinear autoregression neural network NARX, which confirmed HMTS efficiency.

scholarly journals Modeling of Lake Malombe Annual Fish Landings and Catch per Unit Effort (CPUE)

Forecasting ◽  
2021 ◽  
Vol 3 (1) ◽  
pp. 39-55
Author(s):  
Rodgers Makwinja ◽  
Seyoum Mengistou ◽  
Emmanuel Kaunda ◽  
Tena Alemiew ◽  
Titus Bandulo Phiri ◽  
...  
Keyword(s):  
Time Series ◽  
Information Criterion ◽  
Coefficient Of Determination ◽  
Series Data ◽  
Efficiency Coefficient ◽  
Arima Models ◽  
Catch Per Unit Effort ◽  
Annual Fish ◽  
Unit Effort ◽  
The Mean

Forecasting, using time series data, has become the most relevant and effective tool for fisheries stock assessment. Autoregressive integrated moving average (ARIMA) modeling has been commonly used to predict the general trend for fish landings with increased reliability and precision. In this paper, ARIMA models were applied to predict Lake Malombe annual fish landings and catch per unit effort (CPUE). The annual fish landings and CPUE trends were first observed and both were non-stationary. The first-order differencing was applied to transform the non-stationary data into stationary. Autocorrelation functions (AC), partial autocorrelation function (PAC), Akaike information criterion (AIC), Bayesian information criterion (BIC), square root of the mean square error (RMSE), the mean absolute error (MAE), percentage standard error of prediction (SEP), average relative variance (ARV), Gaussian maximum likelihood estimation (GMLE) algorithm, efficiency coefficient (E2), coefficient of determination (R2), and persistent index (PI) were estimated, which led to the identification and construction of ARIMA models, suitable in explaining the time series and forecasting. According to the measures of forecasting accuracy, the best forecasting models for fish landings and CPUE were ARIMA (0,1,1) and ARIMA (0,1,0). These models had the lowest values AIC, BIC, RMSE, MAE, SEP, ARV. The models further displayed the highest values of GMLE, PI, R2, and E2. The “auto. arima ()” command in R version 3.6.3 further displayed ARIMA (0,1,1) and ARIMA (0,1,0) as the best. The selected models satisfactorily forecasted the fish landings of 2725.243 metric tons and CPUE of 0.097 kg/h by 2024.

scholarly journals Correlation between Acinetobacter baumannii Resistance and Hospital Use of Meropenem, Cefepime, and Ciprofloxacin: Time Series Analysis and Dynamic Regression Models

Pathogens ◽  
2021 ◽  
Vol 10 (4) ◽  
pp. 480
Author(s):  
Rania Kousovista ◽  
Christos Athanasiou ◽  
Konstantinos Liaskonis ◽  
Olga Ivopoulou ◽  
George Ismailos ◽  
...  
Keyword(s):  
Time Series ◽  
Time Series Analysis ◽  
Acinetobacter Baumannii ◽  
Regression Models ◽  
Tertiary Care ◽  
Antimicrobial Use ◽  
Care Hospital ◽  
Arima Models ◽  
Series Analysis ◽  
Resistance Rates

Acinetobacter baumannii is one of the most difficult-to-treat pathogens worldwide, due to developed resistance. The aim of this study was to evaluate the use of widely prescribed antimicrobials and the respective resistance rates of A. baumannii, and to explore the relationship between antimicrobial use and the emergence of A. baumannii resistance in a tertiary care hospital. Monthly data on A. baumannii susceptibility rates and antimicrobial use, between January 2014 and December 2017, were analyzed using time series analysis (Autoregressive Integrated Moving Average (ARIMA) models) and dynamic regression models. Temporal correlations between meropenem, cefepime, and ciprofloxacin use and the corresponding rates of A. baumannii resistance were documented. The results of ARIMA models showed statistically significant correlation between meropenem use and the detection rate of meropenem-resistant A. baumannii with a lag of two months (p = 0.024). A positive association, with one month lag, was identified between cefepime use and cefepime-resistant A. baumannii (p = 0.028), as well as between ciprofloxacin use and its resistance (p < 0.001). The dynamic regression models offered explanation of variance for the resistance rates (R2 > 0.60). The magnitude of the effect on resistance for each antimicrobial agent differed significantly.

scholarly journals Time Series Analysis of Climatic Variables in Peninsular Spain. Trends and Forecasting Models for Data between 20th and 21st Centuries

Climate ◽  
2021 ◽  
Vol 9 (7) ◽  
pp. 119
Author(s):  
Pitshu Mulomba Mukadi ◽  
Concepción González-García
Keyword(s):  
Time Series ◽  
Temperature Series ◽  
Climatic Variables ◽  
Monthly Precipitation ◽  
Agricultural Crops ◽  
Arima Models ◽  
Cover Time ◽  
Pests And Diseases ◽  
Historical Series ◽  
Preliminary Phase

Time series of mean monthly temperature and total monthly precipitation are two of the climatic variables most easily obtained from weather station records. There are many studies analyzing historical series of these variables, particularly in the Spanish territory. In this study, the series of these two variables in 47 stations of the provincial capitals of mainland Spain were analyzed. The series cover time periods from the 1940s to 2013; the studies reviewed in mainland Spain go up to 2008. ARIMA models were used to represent their variation. In the preliminary phase of description and identification of the model, a study to detect possible trends in the series was carried out in an isolated manner. Significant trends were found in 15 of the temperature series, and there were trends in precipitation in only five of them. The results obtained for the trends are discussed with reference to those of other, more detailed studies in the different regions, confirming whether the same trend was maintained over time. With the ARIMA models obtained, 12-month predictions were made by measuring errors with the observed data. More than 50% of the series of both were modeled. Predictions with these models could be useful in different aspects of seasonal job planning, such as wildfires, pests and diseases, and agricultural crops.

scholarly journals An External Agribusiness Risk Analysis Using KBDI: A Case of Veldfires in the Northern Territory of Australia

2020 ◽  
Vol 3 (1) ◽  
pp. 37
Author(s):  
Toyi Maniki Diphagwe ◽  
Bernard Moeketsi Hlalele ◽  
Dibuseng Priscilla Mpakathi
Keyword(s):  
Time Series ◽  
Probability Distribution ◽  
Crop Production ◽  
Time Series Data ◽  
Northern Territory ◽  
Return Level ◽  
Series Data ◽  
Burned Area ◽  
Trend Test ◽  
P Value

The 2019/20 Australian bushfires burned over 46 million acres of land, killed 34 people and left 3500 individuals homeless. Majority of deaths and buildings destroyed were in New South Wales, while the Northern Territory accounted for approximately 1/3 of the burned area. Many of the buildings that were lost were farm buildings, adding to the challenge of agricultural recovery that is already complex because of ash-covered farmland accompanied by historic levels of drought. The current research therefore aimed at characterising veldfire risk in the study area using Keetch-Byram Drought Index (KBDI). A 39-year-long time series data was obtained from an online NASA database. Both homogeneity and stationarity tests were deployed using a non-parametric Pettitt’s and Dicky-Fuller tests respectively for data quality checks. Major results revealed a non-significant two-tailed Mann Kendall trend test with a p-value = 0.789 > 0.05 significance level. A suitable probability distribution was fitted to the annual KBDI time series where both Kolmogorov-Smirnov and Chi-square tests revealed Gamma (1) as a suitably fitted probability distribution. Return level computation from the Gamma (1) distribution using XLSTAT computer software resulted in a cumulative 40-year return period of moderate to high fire risk potential. With this low probability and 40-year-long return level, the study found the area less prone to fire risks detrimental to animal and crop production. More agribusiness investments can safely be executed in the Northern Territory without high risk aversion.

scholarly journals E MUSCULOSKELETAL DISORDERS (MS PENGARUH WORKPLACE STRETCHING EXERCISE TERHADAP PENURUNAN KELUHAN MUSCULOSKELETAL DISORDERS(MSDs) PADA PEKERJA BATIK TULIS DI KECAMATAN SOKARAJA

2017 ◽  
Vol 9 (02) ◽  
pp. 49
Author(s):  
Siti Harwanti ◽  
Nur Ulfah ◽  
Budi Aji
Keyword(s):  
Musculoskeletal Disorders ◽  
Wilcoxon Test ◽  
Control Group ◽  
P Value ◽  
Control Group Design ◽  
Test Analysis ◽  
Result Show ◽  
Post Test ◽  
Equivalent Control ◽  
And Control

Batik maked process especially �mbironi�, is done in sit position. If this position maintained for a long period, that could be cause muscle strain which may lead into musculoskeletal disorders. The research was aim to know the effect of Workplace Stretching Exercise (WSE) to reduced MSDs in hand-made batik workers. The research was quasy experimental by non-equivalent control group design. Subjects were 37 female handmade batik workers used purposive sampling. Data analysis used Friedman test and Wilcoxon test, then for two independent sample used Independent t Test and Mann Whitney test with significancy level at 5% or a = 0,05. Analysis result show that there is no difference in MSDs on experiment and control group after pre-test which had p-value = 0,371 (>0,05). The result of middle-test and post-test p value = 0,000 (<0,05) that there is significant mean difference of MSDs between experiment and control group. Based on the middle-test and post-test analysis result, it could be conclude that there is an effect of WSE to reduce MSDs of handmade batik workers.

scholarly journals Finite-sample genome-wide regression p-values (GWRPV) with a non-normally distributed phenotype

2017 ◽  
Author(s):  
Gregory Connor ◽  
Michael O’Neill
Keyword(s):  
Central Limit Theorem ◽  
Limit Theorem ◽  
Central Limit ◽  
Mixture Distribution ◽  
Small Sample ◽  
P Value ◽  
Finite Sample ◽  
P Values ◽  
Genome Wide ◽  
Normally Distributed

AbstractThis paper derives the exact finite-sample p-value for univariate regression of a quantitative phenotype on individual genome markers, relying on a mixture distribution for the dependent variable. The p-value estimator conventionally used in existing genome-wide association study (GWAS) regressions assumes a normally-distributed dependent variable, or relies on a central limit theorem based approximation. The central limit theorem approximation is unreliable for GWAS regression p-values, and measured phenotypes often have markedly non-normal distributions. A normal mixture distribution better fits observed phenotypic variables, and we provide exact small-sample p-values for univariate GWAS regressions under this flexible distributional assumption. We illustrate the adjustment using a years-of-education phenotypic variable.

scholarly journals EFEKTIVITAS PENERAPAN TERAPI PERMAINAN SOSIALISASI UNTUK MENURUNKAN PERILAKU IMPULSIF PADA ANAK DENGAN ATTENTION DEFICIT HYPERACTIVE DISORDER (ADHD)

2012 ◽  
Vol 3 (1) ◽  
pp. 67
Author(s):  
Deyla Erinta ◽  
Meita Santi Budiani
Keyword(s):  
Time Series ◽  
Play Therapy ◽  
Rating Scale ◽  
Rank Test ◽  
P Value ◽  
Impulsive Behavior ◽  
Research Subjects ◽  
Children With Adhd ◽  
Purposive Sampling ◽  
Series Design

The purpose of this study was to examine the effectiveness of socialization play therapy to reducing impulsive behavior in children. The research subjects are kinder garten students in SLB N Gedangan Sidoarjo. This study used a quantitative method along with Quasi experiment design with the type of Time Series Design. Purposive sampling techniques was used to collect the research  subjects that has the characteristics of subjects with ADHD. Data collection method used Rating scale of impulsive behavior children with ADHD and using Wilcoxon signed rank test. The result of data  analysis obtained P-value or sig at 0,043 with α = 0,05. It means that H0 is rejected and this the H1 accepted. It can be concluded that the application of socialization play therapy is effective to reduce impulsive behavior in children with ADHD on SLB N Gedangan Sidoarjo.Abstrak: Penelitian ini bertujuan untuk menguji seberapa efektif terapi permainan sosialisasi dalam menurunkan perilaku impulsif pada anak ADHD. Subjek penelitian adalah siswa TK di SLB N Gedangan, Sidoarjo. Penelitian ini menggunakan metode kuantitatif dengan desain Quasi Eksperiment dengan jenis Time Series Design. Pengambilan subjek penelitian menggunakan teknik purposive sampling yakni subjek yang memiliki karakteristik subjek yang mengalami ADHD. Pengumpulan data menggunakan rating scale perilaku impulsif pada anak ADHD dan menggunakan Wilcoxon sign rank test. Hasil analisis data diperoleh nilai P - value atau sig sebesar 0,043 dengan taraf α = 0,05. Artinya H0 ditolak dan hal ini menunjukkan bahwa H1 diterima. Dengan demikian, dapat disimpulkan bahwa penerapan terapi permainan sosialisasi efektif untuk menurunkan perilaku impulsif pada anak ADHD di SLB N Gedangan, Sidoarjo.           

scholarly journals Faktor-Faktor Yang Mempengaruhi Volume Ekspor Teh PTPN IX, Jawa Tengah

2018 ◽  
Vol 14 (2) ◽  
pp. 167
Author(s):  
Agnes Chaprilia ◽  
Yuliawati Yuliawati
Keyword(s):  
Time Series ◽  
Export Price ◽  
Secondary Data ◽  
Research Use ◽  
Analysis Technique ◽  
Export Volume ◽  
Result Show ◽  
Negative Effect ◽  
Quantitative Descriptive ◽  
Coffee Price

<p><em>The purpose of this reasearch is to 1) analyze the factors that influence tea export volume of PTPN IX, 2) to know the overview of PTPN IX tea export trend and forecasting. The kind of this research is quantitative descriptive and use secondary data that sourced from related agencies and organizations. Data in form time series during 96 months from January 2010 until December 2017 are used in this research. This research use multiple linear regression and ARIMA (Box-Jenkin) as analysis technique.  Regression  analysis  result  show  tea  export  price,  coffee  price,  and exchange rate had a negative effect and significant for export volume with value R</em><em>2</em><em> i</em><em>s 0,479. Trend analysis use ARIMA shown period of tea export volume from January</em><em> 2010 until December 2017 had fluctuated and shown a downward trend.</em></p>

scholarly journals A case study of the residual-based cointegration procedure

2003 ◽  
Vol 7 (1) ◽  
pp. 29-48
Author(s):  
Riccardo Biondini ◽  
Yan-Xia Lin ◽  
Michael Mccrae
Keyword(s):  
Time Series ◽  
Financial Time Series ◽  
Real Life ◽  
Arima Models ◽  
Financial Time ◽  
Long Run ◽  
Data Generating Process ◽  
Finance Theory ◽  
The Relationship

The study of long-run equilibrium processes is a significant component of economic and finance theory. The Johansen technique for identifying the existence of such long-run stationary equilibrium conditions among financial time series allows the identification of all potential linearly independent cointegrating vectors within a given system of eligible financial time series. The practical application of the technique may be restricted, however, by the pre-condition that the underlying data generating process fits a finite-order vector autoregression (VAR) model with white noise. This paper studies an alternative method for determining cointegrating relationships without such a pre-condition. The method is simple to implement through commonly available statistical packages. This ‘residual-based cointegration’ (RBC) technique uses the relationship between cointegration and univariate Box-Jenkins ARIMA models to identify cointegrating vectors through the rank of the covariance matrix of the residual processes which result from the fitting of univariate ARIMA models. The RBC approach for identifying multivariate cointegrating vectors is explained and then demonstrated through simulated examples. The RBC and Johansen techniques are then both implemented using several real-life financial time series.

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