scholarly journals Time Series Analysis of Indian Spices Export and Prices

2019 ◽  
Author(s):  
S. Anusha ◽  
B. Srinivasa Kumar ◽  
D. Satish Kumar
Keyword(s):  
Time Series ◽  
Hybrid Model ◽  
Time Series Data ◽  
Secondary Data ◽  
Network Models ◽  
Policy Formulation ◽  
Hybrid Models ◽  
Series Data ◽  
Agricultural Commodity ◽  
Agricultural Exports

India is the land of spices and is the largest producer, consumer and the exporter of spices in the world. Spices are an important component of Indian Agricultural Exports earning valuable foreign exchange and are the source of livelihood for millions of small and marginal farmers across different states of the country. Modeling of agricultural exports in general and spices exports in particular is important in the contest of spices exports being a priority area for Indian policy makers. Time series modeling of agricultural commodity exports is an active area of research in recent times. Generally Box Jenkins approach (ARIMA) is the referred technique for this purpose. When data exhibits volatility clustering, ARCH/GARCH models are used .When the data does not support linearity assumptions neutral network models are used. However, real world time series data is believed to be a combination of linear and non-linear patterns. In this context, Hybrid models which are a combination of AR models and Artificial Neural Networks are providing more accurate forecasts. The present study, using secondary data for the period from 1960-61 to 2017-18 applies three hybrid models for forecasting Indian spices exports both in terms of volume and prices. Based on the RMSE each model is evaluated and finally model with least RMSE was selected for forecasting both volume and unit prices of total spices export for the coming 10 years (2018-19 to 2027-28). Analysis of data was done with the help of open source software R. Results from the study show that, Hybrid model consisting of ARIMA, Exponential Smoothing and Tbats Model with unequal weights was found to be the best model on the basis of RMSE for forecasting Indian spices exports. Thus, for both forecasting and policy formulation the hybrid model is recommended.

scholarly journals Development of Multidecomposition Hybrid Model for Hydrological Time Series Analysis

Complexity ◽  
2019 ◽  
Vol 2019 ◽  
pp. 1-14 ◽  
Author(s):  
Hafiza Mamona Nazir ◽  
Ijaz Hussain ◽  
Muhammad Faisal ◽  
Alaa Mohamd Shoukry ◽  
Showkat Gani ◽  
...  
Keyword(s):  
Time Series ◽  
Hybrid Model ◽  
Prediction Accuracy ◽  
Optimal Allocation ◽  
Time Series Data ◽  
Hybrid Models ◽  
Indus Basin ◽  
Series Data ◽  
Stage Model ◽  
Two Stage

Accurate prediction of hydrological processes is key for optimal allocation of water resources. In this study, two novel hybrid models are developed to improve the prediction precision of hydrological time series data based on the principal of three stages as denoising, decomposition, and decomposed component prediction and summation. The proposed architecture is applied on daily rivers inflow time series data of Indus Basin System. The performances of the proposed models are compared with traditional single-stage model (without denoised and decomposed), the hybrid two-stage model (with denoised), and existing three-stage hybrid model (with denoised and decomposition). Three evaluation measures are used to assess the prediction accuracy of all models such as Mean Relative Error (MRE), Mean Absolute Error (MAE), and Mean Square Error (MSE). The proposed, three-stage hybrid models have shown improvement in prediction accuracy with minimum MRE, MAE, and MSE for all case studies as compared to other existing one-stage and two-stage models. In summary, the accuracy of prediction is improved by reducing the complexity of hydrological time series data by incorporating the denoising and decomposition.

scholarly journals FAKTOR – FAKTOR YANG MEMPENGARUHI KONSUMSI GAS DI INDONESIA

2021 ◽  
Vol 3 (2) ◽  
pp. 69
Author(s):  
Rohim Rohim ◽  
Mike Triani
Keyword(s):  
Time Series ◽  
World Bank ◽  
Time Series Data ◽  
Secondary Data ◽  
Series Data ◽  
Industrial Growth ◽  
A Value ◽  
The World Bank ◽  
The World ◽  
Gas Consumption

The purpose of this research is to determine (1) the effect of income on gas consumption in Indonesia (2) the effect of population on gas consumption in Indonesia (3) the effect of industrial growth on gas consumption in Indonesia. This type of research is descriptive and associative. The data used in this research is secondary data from Indonesia in the form of time series data from 1970 to 2019 and this data was obtained from official institutions of the World Bank and BP Statistic World. The data were processed using multiple linear regression. The results showed that the income had a negative and significant effect on gas consumption with a probability value of 0.0005 <0.05, the population had a positive and significant effect on gas consumption with a value of prob t-count of 0.0010 <0.05 and industrial growth had a positive and significant effect on gas consumption.  The significant to gas consumption in Indonesia with a value of prob t-count value of 0.5219 <0.05 and suggestions for further researchers to be able to analyze other factors that affecting gas consumption in Indonesia.  Because from the gas sectors, there are still many factors that affected gas consumption until the research results will be better

scholarly journals Faktor-Faktor yang Mempengaruhi Alih Fungsi Lahan Sawah di Kabupaten Aceh Besar

2018 ◽  
Vol 3 (4) ◽  
pp. 525-533
Author(s):  
Raudhatul Husna ◽  
Azhar Azhar ◽  
Edy Marsudi
Keyword(s):  
Time Series ◽  
Population Density ◽  
Rice Field ◽  
Time Series Data ◽  
Statistical Tests ◽  
Secondary Data ◽  
Series Data ◽  
Factors Affecting ◽  
Rice Productivity ◽  
Negative Impacts

Abstrak. Alih fungsi lahan atau lazimnya disebut sebagai konversi lahan adalah  perubahan fungsi sebagian atau seluruh kawasan lahan dari fungsinya semula (seperti yang direncanakan) menjadi fungsi lain yang membawa dampak negatif terhadap lingkungan dan potensi lahan itu sendiri. Penelitian ini bertujuan untuk mengetahui apakah harga lahan, kepadatan penduduk, produktivitas padi dan jumlah PDRB dapat mempengaruhi alih fungsi lahan sawah di Kabupaten Aceh Besar. Data yang digunakan dalam penelitian ini adalah data sekunder. Data yang dikumpulkan adalah data time series dengan range tahun 2002 sampai 2016. Penelitian ini menggunakan metode analisis  regresi linier berganda. hasil penelitian dan pembahasan serta pengujian SPSS menunjukkan bahwa harga lahan, kepadatan penduduk, dan produktivitas padi berpengaruh nyata terhadap alih fungsi lahan sawah di Kabupaten Aceh Besar. sedangkan jumlah PDRB tidak berpengaruh terhadap alih fungsi lahan sawah. Hal ini ditunjukkan oleh koefisien regresi untuk variabel jumlah PDRB sebesar 0,00015. Hasil pengujian statistik menunjukkan nilai t hitung untuk jumlah PDRB sebesar 1,315 dengan nilai signifikan sebesar 0,218. Sedangkan nilai t tabel sebesar 1,782 yang berarti nilai t hitung t tabel (1,315 1,782).  Factors Affecting The Conversion Of Paddy Fields In Kabupaten Aceh Besar Abstract. Land use change or commonly referred to as land conversion is a change in the function of part or all of the land area from its original function (as planned) into other functions that bring negative impacts to the environment and the potential of the land itself. This study aims to find out whether the price of land, population density, rice productivity and the amount of GRDP can affect the conversion of rice field functions in Aceh Besar District. The data used in this research is secondary data. The data collected is time series data with range of year 2002 until 2016. This research use multiple linier regression analysis method. the results of research and discussion and testing of SPSS showed that land price, population density, and rice productivity significantly affected the conversion of wetland in Aceh Besar district. while the number of GDP does not affect the conversion of wetland. This is indicated by the regression coefficient for the GRDP variable of 0.00015. The results of statistical tests show the value of t arithmetic for the amount of GRDP by 1.315 with a significant value of 0.218. While the value of t table of 1.782 which means the value of t arithmetic t table (1,315 1.782).

scholarly journals PENGARUH ZIS (ZAKAT, INFAK, SEDEKAH) DAN ZAKAT FITRAH TERHADAP PENURUNAN KEMISKINAN DI INDONESIA PERIODE 1998 - 2010

Media Ekonomi ◽  
2017 ◽  
Vol 20 (1) ◽  
pp. 83
Author(s):  
Jumadin Lapopo
Keyword(s):  
Time Series ◽  
Developing Countries ◽  
Regression Analysis ◽  
Poverty Reduction ◽  
Time Series Data ◽  
Secondary Data ◽  
Series Data ◽  
Analysis Model ◽  
Independent Variables

<p>Poverty is being a problem in all developing countries including Indonesia. Among goverment programs, poverty has become the center offattention in policy at both of the regional and national levels. Looking at thephenomenon of poverty, Islam present with solution to reduce poverty through Zakat. This study aims to analyze the effect of ZIS and Zakat Fitrah against poverty in Indonesia in 1998 until 2010, data used in this study is secondary data and uses time series data, for the dependent variabel is poverty and for independent variables are ZIS and Zakat Fitrah. The analysis tools used in this study is to use multiple regression analysis model and the assumptions of classical test using the software Eviews-4. In this study also concluded that the ZIS variables significantly affect to the reduction of poverty in Indonesia although the effect is very small. In the variable Zakat Fitrah not significantly affect poverty reduction in Indonesia because of the nature of Zakat Fitrah is for consumption and not for long-term needs. The results of this study can be used for the management of zakat to be able to develop the management and to get a better system for distribution of zakat so that the main purpose of zakat can be achieved to reduce poverty.<br />Keywords : Poverty, Zakat Fitrah, ZIS.</p>

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.

Inferring Gene Regulatory Network Models from Time-Series Data Using Metaheuristics

2020 ◽  
Author(s):  
Jose Eduardo H. da Silva ◽  
Heder S. Betnardino ◽  
Helio J.C. Barbosa ◽  
Alex B. Vieira ◽  
Luciana C.D. Campos ◽  
...  
Keyword(s):  
Time Series ◽  
Gene Regulatory Network ◽  
Regulatory Network ◽  
Time Series Data ◽  
Network Models ◽  
Series Data ◽  
Gene Regulatory

scholarly journals Medical service demand forecasting using a hybrid model based on ARIMA and self-adaptive filtering method

2020 ◽  
Vol 20 (1) ◽  
Author(s):  
Yihuai Huang ◽  
Chao Xu ◽  
Mengzhong Ji ◽  
Wei Xiang ◽  
Da He
Keyword(s):  
Time Series ◽  
Hybrid Model ◽  
Adaptive Filtering ◽  
Time Series Data ◽  
Arima Model ◽  
Series Data ◽  
Short Term ◽  
Hybrid Prediction ◽  
Service Demand ◽  
Filtering Method

Abstract Background Accurate forecasting of medical service demand is beneficial for the reasonable healthcare resource planning and allocation. The daily outpatient volume is characterized by randomness, periodicity and trend, and the time series methods, like ARIMA are often used for short-term outpatient visits forecasting. Therefore, to further enlarge the prediction horizon and improve the prediction accuracy, a hybrid prediction model integrating ARIMA and self-adaptive filtering method is proposed. Methods The ARIMA model is first used to identify the features like cyclicity and trend of the time series data and to estimate the model parameters. The parameters are then adjusted by the steepest descent algorithm in the adaptive filtering method to reduce the prediction error. The hybrid model is validated and compared with traditional ARIMA by several test sets from the Time Series Data Library (TSDL), a weekly emergency department (ED) visit case from literature study, and the real cases of prenatal examinations and B-ultrasounds in a maternal and child health care center (MCHCC) in Ningbo. Results For TSDL cases the prediction accuracy of the hybrid prediction is improved by 80–99% compared with the ARIMA model. For the weekly ED visit case, the forecasting results of the hybrid model are better than those of both traditional ARIMA and ANN model, and similar to the ANN combined data decomposition model mentioned in the literature. For the actual data of MCHCC in Ningbo, the MAPE predicted by the ARIMA model in the two departments was 18.53 and 27.69%, respectively, and the hybrid models were 2.79 and 1.25%, respectively. Conclusions The hybrid prediction model outperforms the traditional ARIMA model in both accurate predicting result with smaller average relative error and the applicability for short-term and medium-term prediction.

scholarly journals Hybrid methodology for tuberculosis incidence time-series forecasting based on ARIMA and a NAR neural network

2017 ◽  
Vol 145 (6) ◽  
pp. 1118-1129 ◽  
Author(s):  
K. W. WANG ◽  
C. DENG ◽  
J. P. LI ◽  
Y. Y. ZHANG ◽  
X. Y. LI ◽  
...  
Keyword(s):  
Neural Network ◽  
Time Series ◽  
Hybrid Model ◽  
Prevention And Control ◽  
Time Series Data ◽  
Arima Model ◽  
Public Health Problem ◽  
Series Data ◽  
Percentage Error ◽  
And Control

SUMMARYTuberculosis (TB) affects people globally and is being reconsidered as a serious public health problem in China. Reliable forecasting is useful for the prevention and control of TB. This study proposes a hybrid model combining autoregressive integrated moving average (ARIMA) with a nonlinear autoregressive (NAR) neural network for forecasting the incidence of TB from January 2007 to March 2016. Prediction performance was compared between the hybrid model and the ARIMA model. The best-fit hybrid model was combined with an ARIMA (3,1,0) × (0,1,1)12 and NAR neural network with four delays and 12 neurons in the hidden layer. The ARIMA-NAR hybrid model, which exhibited lower mean square error, mean absolute error, and mean absolute percentage error of 0·2209, 0·1373, and 0·0406, respectively, in the modelling performance, could produce more accurate forecasting of TB incidence compared to the ARIMA model. This study shows that developing and applying the ARIMA-NAR hybrid model is an effective method to fit the linear and nonlinear patterns of time-series data, and this model could be helpful in the prevention and control of TB.

scholarly journals Pengelolaan Pajak Daerah dan Retribusi Daerah Kota Tasikmalaya Tahun 2016-2019

2020 ◽  
Vol 7 (02) ◽  
pp. 197-202
Author(s):  
Betanika Nila Nirbita ◽  
Sri Hardianti Sartika
Keyword(s):  
Time Series ◽  
Local Government ◽  
Time Series Data ◽  
Secondary Data ◽  
Jel Classification ◽  
Series Data ◽  
Government Revenue ◽  
Regional Autonomy ◽  
Local Revenue ◽  
Local Taxes

ABSTRACT        Original Local Government Revenue is the income used by regional autonomy to fund the implementation of regional autonomy in accordance with each region's potential. Local taxes and retribution are part of local revenue. This study aims to determine the effectiveness and contribution of local taxes and retribution to the Local Government Revenue of Tasikmalaya. This research is a descriptive study using Time Series-type secondary data source. Secondary data comes from the 2016-2019 Budget Realization and Target report of OLGR of Tasikmalaya Region. The results of this study indicate that the highest level of regional tax effectiveness in Tasikmalaya by 2019 was 106.18% and the lowest value is in 2016 at 101.6%, while the highest level of effec-tiveness of regional retribution was in 2016, at 105.33%, and the lowest was 2019 at 90.92%. The contribution of local taxes to Tasikmalaya local revenue reached the high-est in 2016 with 88.59% while the lowest was in 2019, at 49.37%. In the other hand, the highest contribution to regional retribution was in 2016 at 11.4% and the lowest in 2019, at 3, 83%.. ABSTRAK         Pendapatan Asli Daerah (PAD) merupakan pendapatan yang digunakan oleh otonomi daerah untuk mendanai pelaksanaan otonomi daerah sesuai dengan potensi daerah masing-masing. Pajak daerah dan retribusi daerah merupakan bagian dari Pendapatan Asli Daerah. Penelitian ini bertujuan untuk mengetahui efektivitas dan kontribusi pajak daerah dan retribusi daerah terhadap Pendapatan Asli Daerah Kota Tasikmalaya. Penelitian ini merupakan penelitian diskriptif dengan menggunakan sumber data sekunder tipe Time Series. Data sekunder berasal dari laporan Anggaran Realisasi dan Target PAD Kota Tasikmalaya tahun 2016-2019. Hasil penelitian ini menunjukkan bahwa tingkat efektivitas tertinggi pajak daerah Kota Tasikmalaya pada tahun 2019 yaitu 106,18% dan yang paling rendah tahun 2016 yaitu 101,6%, sedangkan tingkat efektivitas retribusi daerah tertinggi pada tahun 2016 yaitu 105,33% dan paling rendah tahun 2019 yaitu 90,92%. Kontribusi pajak daerah terhadap pendapatan asli daerah Kota Tasikmalaya tertinggi pada tahun 2016 yaitu 88,59 dan paling rendah pada tahun 2019 yaitu 49,37%, sedangkan kontribusi retribusi daerah tertinggi pada tahun 2016 yaitu 11,4% dan paling rendah tahun 2019 yaitu 3,83%. JEL Classification : H27, H30

Sign in / Sign up

Export Citation Format

Share Document