scholarly journals PERAMALAN EKSPOR NONMIGAS DENGAN VARIASI KALENDER ISLAM MENGGUNAKAN X-13-ARIMA-SEATS (Studi Kasus: Ekspor Nonmigas Periode Januari 2013 sampai Desember 2017)

2018 ◽  
Vol 7 (3) ◽  
pp. 236-247
Author(s):  
Eka Lestari ◽  
Tatik Widiharih ◽  
Rita Rahmawati
Keyword(s):  
Time Series ◽  
Linear Regression ◽  
Foreign Exchange ◽  
Decomposition Method ◽  
Oil And Gas ◽  
Time Series Data ◽  
Series Data ◽  
Holiday Effect ◽  
Forecasting Method ◽  
Loading And Unloading

Non-oil and gas exports are one of the largest foreign exchange earners for Indonesia. Non-oil and gas exports always experience a decline in the month of Eid Al-Fitr due to delays in the delivery of export goods because the loading and unloading of goods at the port is reduced during Eid Al-Fitr. The shift of the Eid Al-Fitr month on the data will form a pattern or season with an unequal period called the moving holiday effect. The time series forecasting method that usually used the ARIMA method. Because the ARIMA method only suitable for time series data with the same seasonal period and can’t handle the moving holiday effect, the X-13-ARIMA-SEATS method used two steps. First, regARIMA modeling is a linear regression between time series data and the weight of Eid Al-Fitr and the residuals follow the ARIMA process. The weighting is based on three conditions, namely pre_holiday, post_holiday, and multiple. Second, X-12-ARIMA decomposition method for seasonal adjustments that produces trend-cycle components, seasonal, and irregular. Based on the analysis carried out on the monthly non-oil and gas export data for the period January 2013 to December 2017, the X-13-ARIMA-SEATS (1,1,0) model was obtained in the post_holiday condition as the best model. The forecasting results in 2018 show the largest decline in non-oil and gas exports in June 2018 which coincided with the Eid Al-Fitr holiday. MAPE value of 10.90% is obtained which shows that the forecasting ability is good.Keywords:  time series, non-oil and gas, X-13-ARIMA-SEATS, moving holiday

scholarly journals Load forecasting of refrigerated display cabinet based on CEEMD–IPSO–LSTM combined model

Open Physics ◽  
2021 ◽  
Vol 19 (1) ◽  
pp. 360-374
Author(s):  
Yuan Pei ◽  
Lei Zhenglin ◽  
Zeng Qinghui ◽  
Wu Yixiao ◽  
Lu Yanli ◽  
...  
Keyword(s):  
Time Series ◽  
Deep Learning ◽  
Time Series Data ◽  
Load Forecasting ◽  
Series Data ◽  
Forecasting Model ◽  
Combined Model ◽  
Forecasting Accuracy ◽  
Forecasting Method ◽  
Consumption Reduction

Abstract The load of the showcase is a nonlinear and unstable time series data, and the traditional forecasting method is not applicable. Deep learning algorithms are introduced to predict the load of the showcase. Based on the CEEMD–IPSO–LSTM combination algorithm, this paper builds a refrigerated display cabinet load forecasting model. Compared with the forecast results of other models, it finally proves that the CEEMD–IPSO–LSTM model has the highest load forecasting accuracy, and the model’s determination coefficient is 0.9105, which is obviously excellent. Compared with other models, the model constructed in this paper can predict the load of showcases, which can provide a reference for energy saving and consumption reduction of display cabinet.

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 Pengaruh Industri Sedang dan Besar Terhadap Tingkat Kemiskinan di Kabupaten Gresik Tahun 2002-2016

2021 ◽  
Vol 4 (1) ◽  
pp. 25-31
Author(s):  
Rohmatul Janah ◽  
Ida Nuraini
Keyword(s):  
Time Series ◽  
Linear Regression ◽  
Multiple Linear Regression ◽  
Time Series Data ◽  
Industrial Sector ◽  
Poverty Rate ◽  
Domestic Product ◽  
Series Data ◽  
Insignificant Effect ◽  
Poverty Levels

This research is aimed at studying the influence of medium and large industries on poverty levels in Gresik on 2002-2016. The variables used in this study is medium and large industries, a labour of medium and large industries, gross regional domestic product (GRDP) of industrial sector and poverty rate. The method used in this study used multiple linear regression and used time-series data. The results of this study simultaneously are the variables of the amount of medium and large industries, the labour medium and large industries, and the gross regional domestic product (GRDP) of the industrial sector to poverty rate is significant. While medium and large industries to poverty rate have negative and insignificant effect with a coefficient value of -0,208905. The labour of medium and large industries to poverty rate has a positive and significant effect with a coefficient value of 0,130822,  the gross regional domestic product (GRDP) of industrial to poverty rate has a negative and significant effect with a coefficient value of -0,169431.

Pengaruh pertumbuhan ekonomi, inflasi dan pengangguran terhadap tingkat kemiskinan di Sumatera Selatan

2019 ◽  
Vol 16 (1) ◽  
pp. 1-10
Author(s):  
Novegya Ratih Primandari
Keyword(s):  
Economic Growth ◽  
Time Series ◽  
Linear Regression ◽  
Linear Regression Model ◽  
Time Series Data ◽  
Secondary Data ◽  
Poverty Rate ◽  
Least Square ◽  
Series Data ◽  
Ordinary Least Square

This research aims to analyze effect of economic growth, inflation and Unemployment on the Rate of Poverty in the Province of South Sumatera. This research used secondary data in the form of time series data from 2001-2017. The method used quantitative approach by applying a linear regression model with OLS estimation Ordinary Least Square (OLS) method. The results of this study indicate that partially and simultaneously Economic Growth, Inflation and Unemployment have a significant effect on the Poverty Rate in the Province of South Sumatera.

Analysing and forecasting China containerized freight index with a hybrid decomposition–ensemble method based on EMD, grey wave and ARMA

2020 ◽  
Vol ahead-of-print (ahead-of-print) ◽  
Author(s):  
Yanhui Chen ◽  
Bin Liu ◽  
Tianzi Wang
Keyword(s):  
Time Series ◽  
Time Series Data ◽  
Grey System Theory ◽  
Ensemble Method ◽  
Series Data ◽  
Forecasting Model ◽  
Content Type ◽  
Forecasting Method ◽  
Periodic Fluctuations ◽  
Wave Forecasting

PurposeThis paper applied grey wave forecasting in a decomposition–ensemble forecasting method for modelling the complex and non-linear features in time series data. This application aims to test the advantages of grey wave forecasting method in predicting time series with periodic fluctuations.Design/methodology/approachThe decomposition–ensemble method combines empirical mode decomposition (EMD), component reconstruction technology and grey wave forecasting. More specifically, EMD is used to decompose time series data into different intrinsic mode function (IMF) components in the first step. Permutation entropy and the average of each IMF are checked for component reconstruction. Then the grey wave forecasting model or ARMA is used to predict each IMF according to the characters of each IMF.FindingsIn the empirical analysis, the China container freight index (CCFI) is applied in checking prediction performance. Using two different time periods, the results show that the proposed method performs better than random walk and ARMA in multi-step-ahead prediction.Originality/valueThe decomposition–ensemble method based on EMD and grey wave forecasting model expands the application area of the grey system theory and graphic forecasting method. Grey wave forecasting performs better for data set with periodic fluctuations. Forecasting CCFI assists practitioners in the shipping industry in decision-making.

Local linear regression for estimating time series data

2001 ◽  
Vol 37 (2) ◽  
pp. 209-217 ◽  
Author(s):  
Quinton J. Nottingham ◽  
Deborah F. Cook
Keyword(s):  
Time Series ◽  
Linear Regression ◽  
Time Series Data ◽  
Local Linear Regression ◽  
Series Data ◽  
Local Linear

scholarly journals ANALISIS PENGARUH INFLASI, PRODUK DOMESTIK BRUTO (PDB), DAN NILAI TUKAR TERHADAP TABUNGAN MUDHARABAH PADA BANK UMUM SYARIAH (PERIODE 2013-2017)

2019 ◽  
Vol 8 (2) ◽  
pp. 138
Author(s):  
Rita Nur Wahyuningrum ◽  
Aan Zainul Anwar
Keyword(s):  
Time Series ◽  
Exchange Rate ◽  
Linear Regression ◽  
Gross Domestic Product ◽  
Multiple Linear Regression ◽  
Time Series Data ◽  
Domestic Product ◽  
Series Data ◽  
Islamic Banking ◽  
Central Statistics

<p>This study aims to analyze the effect of inflation, gross domestic product (GDP) and rupiah exchange rate on Mudharabah savings in Islamic banking in Indonesia. The data used is time series data for the period March 2013 to September 2017, which was published by Bank Indonesia from the Islamic Banking Statistics Report and the Central Statistics Agency. The technique of analyzing the research is qualitative with the method of Multiple Linear Regression. The results of this study indicate that simultaneously the Inflation, Gross Domestic Product (GDP) and Exchange Rate variables together have a significant effect on Mudharabah Savings. While partially only the Exchange Rate variable has a significant effect on Mudharabah Savings. Inflation Variables and Gross Domestic Product (GDP) have no significant effect on Mudharabah Savings.</p><p> </p><p>Keyword: inflation, gross domestic product, exchange rate, mudharabah saving</p>

scholarly journals Pengaruh Pertumbuhan Ekonomi, Tingkat Inflasi, dan Pertumbuhan Penduduk terhadap Tingkat Pengangguran Terbuka di Indonesia

2019 ◽  
Vol 18 (1) ◽  
pp. 52
Author(s):  
Irma Yuni Astuti ◽  
Nanik Istiyani ◽  
Lilis Yuliati
Keyword(s):  
Economic Growth ◽  
Time Series ◽  
Regression Analysis ◽  
Linear Regression ◽  
Population Growth ◽  
Multiple Linear Regression ◽  
Time Series Data ◽  
Linear Regression Analysis ◽  
Multiple Linear Regression Analysis ◽  
Series Data

This study aims to determine the effect of economic growth, inflation and population growth in open unemployment rate in Indonesia. The type of data used in this study is secondary data in the form of time series data and variable data used are annual data in the period 1986-2017 with the object of research in the country o Indonesia. The data sources used in this study were obtained from the Central Statistics Agency (BPS) Indonesia and World Bank. The analytical method used in this study is multiple linear regression analysis with the Ordinary Least Square (OLS) technique. The estimation of time series data with multiple linear regression analysis shows that the economics growth variable has a positive and not significant effect on the level of open unemployment, the inflation variable has a positive and not significant effect on the level of open unemployment, and the population growth variable has a negative and significant effect on the level of open unemployment in Indonesia. Keywords: Open Unemployment, Economic Growth, Inflation, Population Growth

scholarly journals The Effect of Consumption On The Society Welfare In Sampang District

2020 ◽  
Vol 1 (2) ◽  
Author(s):  
Syamsul Arifin ◽  
Nur Aini Anisa ◽  
Siswohadi Siswohadi ◽  
Aisyah Darti Megasari ◽  
Abu Darim
Keyword(s):  
Time Series ◽  
Linear Regression ◽  
Time Series Data ◽  
Regression Technique ◽  
Quantitative Approach ◽  
Series Data ◽  
Significant Positive Effect ◽  
Economic Stability ◽  
Linear Regression Technique ◽  
Positive Effect

Welfare is one of the most important aspects of maintaining and fostering social and economic stability because it is necessary to minimize social jealousy in society. This study aims to analyze the effect of economic consumption on the welfare of the society in Sampang district. This research uses quantitative approach. This research conducted in Sampang District by using time series data and this research is analyzed by using linear regression technique. According to the result of research indicate that consumption has significant positive effect on the welfare of the society in Sampang district. Based on the results of research that has been conducted, consumption significantly influences the welfare of the society in Sampang district.

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