scholarly journals A Fuzzy ARIMA Correction Model for Transport Volume Forecast

2021 ◽  
Vol 2021 ◽  
pp. 1-10
Author(s):  
Yumeng Xie ◽  
Peilin Zhang ◽  
Yanyi Chen
Keyword(s):  
Time Series ◽  
Predictive Accuracy ◽  
Traffic Volume ◽  
Important Indicator ◽  
Correction Model ◽  
Water Transportation ◽  
Proposed Model ◽  
Inland Waterway ◽  
Transport Volume

In recent years, few water transportation forecasting studies conduct relative to transportation forecasting. As a neglected area, the inland waterway volume prediction is an important indicator for investment management and government policymaking. Considering the time-series forecasting, some researchers try to narrow the predicted value interval. However, certain limitations detract from their popularity. For instance, if the prediction length is more than ten, the result would not be acceptable. Therefore, we propose a hybrid model that combines both of their unique properties’ advantages to provide more accurate traffic volume forecasts. Also, the forecasting process will be more straightforward. The empirical results present the proposed model and improve the long-term predictive accuracy at waterway traffic volume.

scholarly journals A Multivariate Forecasting Model for the COVID-19 Hospital Census Based on Local Infection Incidence

2021 ◽  
Author(s):  
Hieu M. Nguyen ◽  
Philip Turk ◽  
Andrew McWilliams
Keyword(s):  
Time Series ◽  
Multivariate Time Series ◽  
Healthcare Systems ◽  
Leading Indicators ◽  
Short Term ◽  
Correction Model ◽  
Vector Error Correction ◽  
Infection Incidence ◽  
Vector Error

AbstractCOVID-19 has been one of the most serious global health crises in world history. During the pandemic, healthcare systems require accurate forecasts for key resources to guide preparation for patient surges. Fore-casting the COVID-19 hospital census is among the most important planning decisions to ensure adequate staffing, number of beds, intensive care units, and vital equipment. In the literature, only a few papers have approached this problem from a multivariate time-series approach incorporating leading indicators for the hospital census. In this paper, we propose to use a leading indicator, the local COVID-19 infection incidence, together with the COVID-19 hospital census in a multivariate framework using a Vector Error Correction model (VECM) and aim to forecast the COVID-19 hospital census for the next 7 days. The model is also applied to produce scenario-based 60-day forecasts based on different trajectories of the pandemic. With several hypothesis tests and model diagnostics, we confirm that the two time-series have a cointegration relationship, which serves as an important predictor. Other diagnostics demonstrate the goodness-of-fit of the model. Using time-series cross-validation, we can estimate the out-of-sample Mean Absolute Percentage Error (MAPE). The model has a median MAPE of 5.9%, which is lower than the 6.6% median MAPE from a univariate Autoregressive Integrated Moving Average model. In the application of scenario-based long-term forecasting, future census exhibits concave trajectories with peaks lagging 2-3 weeks later than the peak infection incidence. Our findings show that the local COVID-19 infection incidence can be successfully in-corporated into a VECM with the COVID-19 hospital census to improve upon existing forecast models, and to deliver accurate short-term forecasts and realistic scenario-based long-term trajectories to help healthcare systems leaders in their decision making.Author summaryDuring the COVID-19 pandemic, healthcare systems need to have adequate resources to accommodate demand from COVID-19 cases. One of the most important metrics for planning is the COVID-19 hospital census. Only a few papers make use of leading indicators within multivariate time-series models for this problem. We incorporated a leading indicator, the local COVID-19 infection incidence, together with the COVID-19 hospital census in a multivariate framework called the Vector Error Correction model to make 7-day-ahead forecasts. This model is also applied to produce 60-day scenario forecasts based on different trajectories of the pandemic. We find that the two time-series have a stable long-run relationship. The model has a good fit to the data and good forecast performance in comparison with a more traditional model using the census data alone. When applied to different 60-day scenarios of the pandemic, the census forecasts show concave trajectories that peak 2-3 weeks later than the infection incidence. Our paper presents this new model for accurate short-term forecasts and realistic scenario-based long-term forecasts of the COVID-19 hospital census to help healthcare systems in their decision making. Our findings suggest using the local COVID-19 infection incidence data can improve and extend more traditional forecasting models.

scholarly journals Credit Risk Determinants in Indonesia

2018 ◽  
Vol 1 (2) ◽  
pp. 106-115
Author(s):  
Amin Yusuf Efendi
Keyword(s):  
Time Series ◽  
Interest Rate ◽  
Credit Risk ◽  
Error Correction ◽  
Error Correction Model ◽  
Series Data ◽  
Short Term ◽  
Correction Model ◽  
Spurious Regression

Credit is the main business of the banking industry, therefore, in running the business, the bank is always overshadowed by the credit risk the which can be determined by the ratio of non-performing loans (NPL). The development of technology, finance digital brings the outside could impact on the financial industry both positive and negative. The purpose of this study was to analyze the interest rate, inflation, exchange rates, gross domestic product (GDP), a dummy finance digitalization policies in the long term and the short term of the non-performing loan (NPL) of conventional commercial banks in Indonesia The analytical method used in this research is-EG Error Correction Model (ECM), The Data used in this research is secondary quarterly time series data from the 2008 quarter 1-2017 4. The time series of data are not stationar Often that can cause spurious regression results, the exact models used is-EG Error Correction Model (ECM), This models may explain the behavior of short-term and long-term. The results Showed in the short-term variable interest rates significanly to non-performing loans, while in the long-term variable interest rate, exchange rate, and GDP Significantly, non-performing loans. Kredit merupakan bisnis utama dari industri perbankan, oleh karena itu dalam menjalankan bisnisnya, bank selalu dibayangi oleh risiko kredit yang dapat diketahui melalui rasio non-performing loans (NPL). Perkembangan teknologi, menghadirkan digital finance yang membawa dampak luar bisa terhadap industri keuangan baik positif dan negatif. Tujuan dari penelitian ini adalah untuk menganalisis suku bunga, inflasi, kurs, produk domestik bruto (PDB) dummy kebijakan digitalisasi keuangan dalam jangka panjang dan jangka pendek terhadap non-performing loan (NPL) bank umum konvensional di Indonesia  Metode analisis yang digunakan dalam penelitian ini adalah Error Correction Model-EG (ECM). Data yang digunakan dalam penelitian ini adalah data sekunder runtut waktu kuartalan dari 2008 kuartal 1 – 2017 kuartal 4. Data runtun waktu sering tidak stationar sehingga bisa menyebabkan hasil regresi palsu (spurious regression), Model yang tepat digunakan adalah Error Correction Model-EG (ECM), model ini dapat menjelaskan perilaku jangka pendek dan jangka panjang. Hasil penelitian menunjukkan dalam jangka pendek variabel suku bunga berpengaruh secara signifikan terhadap non performing loan, sedangkan dalam jangka panjang variabel suku bunga, kurs, dan PDB berpengaruh secara signifikan terhadap non perfoming loan.

scholarly journals SPURIOUS REGRESSION ANALYSIS ON TIME SERIES DATA FROM FACTORS AFFECTING INDONESIAN HUMAN DEVELOPMENT INDEXS IN 1990 – 2017

2021 ◽  
Vol 7 (3) ◽  
Author(s):  
Rinto Rain Barry ◽  
Innocentius Bernarto
Keyword(s):  
Time Series ◽  
Error Correction ◽  
Human Development ◽  
Time Series Data ◽  
Series Data ◽  
Correction Model ◽  
Independent Variables ◽  
Spurious Regression ◽  
Equilibrium Relationship

In a spurious regression conditions occur linear regression equations that are not stationary on the mean and variance. If the variables are not stationary, there will be cointegration, so it can be concluded that there is a long-term equilibrium relationship between the two research variables and in the short term there is a possibility of an imbalance, so to overcome it in this study using the Error Correction Model. The purpose of this study is to apply a cointegration test to see whether there is a long-term non-equilibrium relationship between the time series between the Human Development Index and life expectancy at birth, average school year for adults aged 25 years and over and gross national income per capita. The data used in this study are time series data between 1990-2017. The statistical management is carried out using Eviews 10. Based on the results obtained, it was concluded that 81.7% and it can be said that the types of independent variables included in the model are already good, because only 18.3% of the diversity of the dependent variable is influenced by the independent variables outside this research model. Keywords: spurious regression, stationary, cointegration, error correction model, equilibrium

scholarly journals Ship Accident Prediction Based on Improved Quantum-Behaved PSO-LSSVM

2020 ◽  
Vol 2020 ◽  
pp. 1-9
Author(s):  
Tian Chai ◽  
Han Xue ◽  
Kaibiao Sun ◽  
Jinxian Weng
Keyword(s):  
Time Series ◽  
Support Vector Machine ◽  
Least Squares ◽  
Traffic Safety ◽  
Management Strategy ◽  
Support Vector ◽  
Important Indicator ◽  
Accident Prediction ◽  
Processing Operation ◽  
Water Transportation

Water transportation plays an important role in the comprehensive transportation system and regional logistics. The number of vessel accidents is an important indicator for evaluating vessel traffic safety and the efficiency of the maritime management strategy. The aim of this work is to provide an efficient way to predict the number of vessel accidents in China. Firstly, to weaken the randomness of the vessel accident number time series, the gray processing operation is adopted to generate a new sequence with exponential and approximate exponential rules. In addition, an extended least-squares support vector machine (LSSVM) model is applied in the forecasting of the new sequence, in which the parameters of the LSSVM are optimized by an improved quantum-behaved particle swarm (IQPSO). The proposed method is applied in the forecasting of the number of vessel accidents in China, and the efficiency is shown by comparing the prediction results with GM (1, 1), PSO-LSSVM, and QPSO-LSSVM.

scholarly journals ANALISIS PENGARUH VARIABEL MAKROEKONOMI DAN COUNTRY RISK TERHADAP INVESTASI PORTOFOLIO ASING DI INDONESIA

2018 ◽  
Vol 7 (2) ◽  
pp. 135
Author(s):  
Halifah Hadi ◽  
Hasdi Aimon ◽  
Dewi Zaini Putri
Keyword(s):  
Economic Growth ◽  
Time Series ◽  
Time Series Data ◽  
Country Risk ◽  
Growth Effect ◽  
Series Data ◽  
Short Term ◽  
Correction Model ◽  
The Stability

The reseach aims to explain the effect of country risk and variabels macroeconomics to the foreign portofolio invesment in Indonesia in short term and long term. The analysis takes time series time series data from 2006 quarter 1 through 2016 quarter 4by using Error Correction Model (ECM). The source of data are Badan Pusat Statistik, Bank Indonesia, FX Sauder and World Bank. The result are in the short term the exchange rate and economic growth effect the shock that will influence the foreign portofolio invesment. In the long trem the inflation, interst rate, money supply and country risk influence on foreign portofolio invesment significanly. The suggestion in this research is, the goverment sould keep the stability balance of payment in Indonesia .Any change, the condition of  balance of payments effect appreciation and depreciation to Rupiah. To increase the economic growth in Indonesia, goverment could increasing the fiscal income and PMDN realization that will  increase the enterprises productivity.

scholarly journals PENGARUH PRODUK DOMESTIK BRUTO, SUKU BUNGA RIIL, DAN PARTISIPASI ANGKATAN KERJA TERHADAP INVESTASI SWASTA DI INDONESIA (Pendekatan Error Correction Model)

2017 ◽  
Vol 3 (1) ◽  
pp. 1-16
Author(s):  
Dedy Syahputra ◽  
Abubakar Hamzah ◽  
Muhammad Nasir
Keyword(s):  
Time Series ◽  
Interest Rates ◽  
Error Correction ◽  
Labor Force ◽  
Private Investment ◽  
Error Correction Model ◽  
Short Term ◽  
Real Interest Rates ◽  
Correction Model

This study aimed to analyze the influence of the GDP, the real interest rate, the labor force, the private investment in Indonesia. The data used in this research is time-series data from 2000 to 2014. The research model uses an error correction model (ECM). The results showed in the long term, GDP, labor force and real interest rates have a statistically significant relationship good and theory with a confidence level of 95 percent. In the long-term estimate found that the labor force will greatly affect private investment and the estimated short-term real interest rates affect the amount of investment that will go to Indonesia. In coefficient explained, the labor force has a strong influence and advice foreign investment into the country. For short-term model estimation results indicate GDP and real interest rates significantly affect the labor force in private investment but no significant effect on private investment. However, both long term and short term, variable real interest rates still the basic reason for investing. In result of cointegration explain that the variable GDP, real interest rates, and the laborforce has a cointegration relation to investment in the long term. The government needs to increase investment and promote the economy and set the interest rates are low. Penelitian ini bertujuan menganalisis pengaruh PDB, tingkat bunga riil, angkatan kerja, terhadap investasi swasta di Indonesia. Data yang digunakan dalam penelitian ini adalah data time-series tahun 2000 hingga tahun 2014. Model penelitian ini menggunakan Error Corection Model (ECM). Hasil menunjukkan dalam jangka panjang, PDB, angkatan kerja dan suku bunga riil memiliki hubungan signifikan baik statistik dan teori dengan tingkat kepercayaan 95 persen. Pada estimasi jangka panjang ditemukan bahwa angkatan kerja akan sangat mempengaruhi investasi swasta dan estimasi jangka pendek, tingkat suku bunga riil mempengaruhi besarnya investasi yang akan masuk ke Indonesia. Secara koefisien menjelaskan, angkatan kerja memiliki pengaruh yang cukup kuat dan memberi masukan investasi asing ke dalam negeri. Untuk hasil estimasi model jangka pendek menunjukkan PDB dan tingkat bunga riil berpengaruh secara signifikan terhadap investasi swasta tetapi angkatan kerja tidak berpengaruh signifikan terhadap investasi swasta. Namun demikian, baik jangka panjang maupun jangka pendek, variabel tingkat suku bunga riil masih menjadi alasan dasar untuk berinvestasi. Dalam hasil kointegrasi menjelaskan bahwa variabel PDB, suku bunga riil, dan angkatan kerja memiliki hubungan kointegrasi terhadap investasi dalam jangka panjang. Pemerintah perlu meningkatkan investasi dan memajukan perekonomian serta mengatur suku bunga yang rendah.

scholarly journals Model Empiris Impor Garam Indonesia

2020 ◽  
Vol 11 (2) ◽  
pp. 125-135
Author(s):  
Abdul Hakim ◽  
Anissa Triyanti
Keyword(s):  
Time Series ◽  
Exchange Rate ◽  
Salt Content ◽  
Correction Model ◽  
Independent Variables ◽  
The World ◽  
Independent Variable ◽  
Very High ◽  
Analyze Data

Indonesia is the fourth longest coastal country in the world, so it has the potential to become a salt exporting country. But appareantly, Indonesia has very high salt imports. This paper seeks to raise the irony bya modeling Indonesian salt imports. Based on various theories and result of previous studies, this paper proposes the price of imported salt, the real exchange rate, the need for salt, and domestic production as an independent variable. The sample used stretches from 2001 to 2018. This paper uses a time series model to analyze data. With the conditional ECM (error correction model), this paper finds that in the short or long term, all selected independent variables have a significant effect on the volume of salt imports, although the exchange rate requires lag to influence the import. This paper suggests increasing the education of salt farmers related to the salt content desired by industry.

COINTEGRATING THE LONG-RUN RELATIONSHIP OF ECONOMIC VARIABLES

2021 ◽  
Vol 14(63) (1) ◽  
pp. 139-152
Author(s):  
Constantin Duguleana
Keyword(s):  
Time Series ◽  
Practical Importance ◽  
Main Tool ◽  
Stationary Time Series ◽  
Correction Model ◽  
Long Run ◽  
Time Variables ◽  
Relationship Of ◽  
Cointegration Relationship

"The economic non-stationary time series often have long-run relationships. The cointegration relationship of time variables describes the continuous adaptation to their equilibrium in the long-run. This paper presents the ways of analysing and modelling the cointegration of time series. The Error Correction Model, as a main tool, and the Engle-Granger method are used to estimate the cointegration in the case of the long-run relationship between the quarterly GDP and the Final Consumption in Romania during the period 1995 – 2019. The practical importance of applying the cointegrating model consists in knowing the effect of GDP in the long term. "

scholarly journals INTERAKSI ASPEK PERMODALAN, RISIKO PEMBIAYAAN, DAN INDIKATOR MAKROEKONOMI DALAM MEMPENGARUHI PROFITABILITAS BPRS DI INDONESIA PERIODE 2014-2018

2020 ◽  
Vol 5 (3) ◽  
pp. 401
Author(s):  
Feri Irawan
Keyword(s):  
Economic Growth ◽  
Time Series ◽  
Error Correction ◽  
Error Correction Model ◽  
Series Data ◽  
Short Term ◽  
Correction Model ◽  
Long Run ◽  
Short And Long Term

<p align="center"><strong><em>ABSTRACT</em></strong></p><p><em>This study aims to analyze the effect of capital aspects (CAR), financing risk (NPF) and macroeconomic variables including economic growth, inflation and the BI Rate on profitability (ROE) in the short and long term. By using time series data for the monthly period from 2013-2018 and the Error-Correction Model (ECM) and cointegration approach, it is found that CAR and NPF do not have a significant effect on ROE in the short and long term. Economic growth, inflation and the BI Rate in the short term do not have a significant effect on ROE, in the long run economic growth, inflation and the BI Rate have a significant effect on ROE. In the short term, economic growth, inflation and the BI Rate disturb the balance of profitability, but in the long run it returns to its equilibrium level. It is necessary to integrate the BPRS policy strategy in managing capital and risk with government policies related to economic growth and inflation.</em></p><p><em> </em></p><p align="center"><strong><em>ABSTRACT</em></strong></p><p><em>Penelitian bertujuan menganalisis pengaruh aspek permodalan (CAR), risiko pembiayaan (NPF) dan variabel makroekonomi yang meliputi pertumbuhan ekonomi, inflasi dam BI Rate  terhadap profitabilitas (ROE) dalam jangka pendek dan jangka panjang. Dengan menggunakan data time series periode bulanan dari tahun 2013-2018 dan pendekatan Error-Correction Model  (ECM) dan kointegrasi, ditemukan bahwa CAR dan NPF tidak berpengaruh secara signifikan terhadap ROE dalam jangka pendek dan jangka panjang. Pertumbuhan ekonomi, inflasi dan BI Rate dalam jangka pendek tidak berpengaruh signifikan terhadap ROE, dalam jangka panjang pertumbuhan ekonomi, inflasi dan BI Rate berpengaruh signfikan terhadap ROE. Pada jangka pendek, pertumbuhan ekonomi, inflasi dan BI Rate menggangu keseimbangan profitabilitas namun dalam jangka panjang kembali pada tingkat keseimbangannya. Diperlukan pengintegrasi strategi kebijakan BPRS dalam mengelola permodalan dan risiko dengan kebijakan pemerintah terkait dengan pertumbuhan ekonomi dan inflasi.</em><em></em></p><p align="right"> </p>

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