Forecasting the Price of Indonesia’s Rice Using Hybrid Artificial Neural Network and Autoregressive Integrated Moving Average (Hybrid NNs-ARIMAX) with Exogenous Variables

Anggraeni, Wiwik; Mahananto, Faizal; Sari, Ayusha Qamara; Zaini, Zulkifli; Andri, Kuntoro Boga; Sumaryanto,

doi:10.1016/j.procs.2019.11.171

Cited by 17 publications

(13 citation statements)

References 9 publications

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“…The second step is transforming the dataset by changing the value range between 0 and 1. The transformation process is carried out using min-max normalization, as shown in (1). This normalization can accelerate the learning process involving data in the same scale value [17].…”

Section: Data Preprocessingmentioning

confidence: 99%

“…In (1), xi is the original value of the i-th data, xi' is the new transformed value of the i-th data, min is the minimum value of all data, and max is the maximum value of all data.…”

Section: Data Preprocessingmentioning

confidence: 99%

“…The number of Indonesian people who consume rice is more than 95%. This number will increase along with the increase in the population of Indonesia [1]. The very high dependence of the Indonesian people on rice has caused the rice to become a benchmark for the Government in determining food-related policies [2].…”

Section: Introductionmentioning

confidence: 99%

See 2 more Smart Citations

Forecasting Model of Staple Food Prices Using Support Vector Regression with Optimized Parameters

Astiningrum

Wijayaningrum

Putri

2021

Jurnal Ilmiah Teknik Elektro Komputer Dan Informatika

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The large number of Indonesians who consume rice as their primary food makes rice price a benchmark for determining the other staple food prices. The instability of rice prices due to climate change or other uncontrollable factors makes it difficult for Indonesians to estimate the rice prices, especially for the poor. This study proposes the usage of the Improved Crow Search Algorithm (ICSA) to optimize the Support Vector Regression (SVR) parameter in building a regression model to predict the price of staple foods. The forecasting process is carried out based on time series data of 11 staples for four years. The proposed ICSA optimizes the six parameters used in the SVR to form a regression model, consisting of lambda, epsilon, sigma, learning rate, soft margin constant, and the number of iterations. Algorithm performance is measured using MAPE and NRMSE by comparing the actual price of staple foods and forecasting results to get the error rate. With this parameter optimization mechanism, the forecasting results given are good enough with a small error value, in the form of MAPE of 17.081 and NRMSE of 1.594. A MAPE value between 10 and 20 indicates that the forecasting result is acceptable, while an NRMSE value of less than 10 indicates that the forecasting accuracy is excellent. The improvised technique on Crow Search Algorithm is proven to improve the performance of Support Vector Regression in forecasting the price of staple foods.

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Section: Data Preprocessingmentioning

confidence: 99%

“…In (1), xi is the original value of the i-th data, xi' is the new transformed value of the i-th data, min is the minimum value of all data, and max is the maximum value of all data.…”

Section: Data Preprocessingmentioning

confidence: 99%

See 1 more Smart Citation

Forecasting Model of Staple Food Prices Using Support Vector Regression with Optimized Parameters

Astiningrum

Wijayaningrum

Putri

2021

Jurnal Ilmiah Teknik Elektro Komputer Dan Informatika

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“…If this is allowed, it will have an impact on the Indonesian economy in the future (Woo & Hong, 2010). The author will use the backpropagation algorithm to predict how much North Sumatra's per capita expenditure will be in the future (Anggraeni et al, 2019). Backpropagationmis one of the methods used in Artificial Neural Networksl(ANN) (Govoruschenko, 2007) which often manages to solve the problem.…”

Section: Introductionmentioning

confidence: 99%

Determining the Best Performance Using the Backpropagation Algorithm for Expenditure per Capita in North Sumatra

Pratama

Solikhun

2022

Computational

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In an effort to maintain per capita income in Indonesia, the Government must take action through strengthening national protection. Per capita is the average income of all residents in a country. Per capita income is obtained from the distribution of the national income of a country by the total population of that country. There is a decrease in the population per capita of North Sumatra at the Central Statistics Agency (BPS) in 2020. The author will use the backpropagation algorithm to make a performance. Backpropagation iskone ofkmethodkartificial neural networklquite reliablejinlsolvekproblem. In researchj5 models are usedlarchitecture: 4-15-1, 4-30-1,k4-45-1, 4-60-1, 4.-75-1, fromjfive modelslThus, the architectural model 4 -75-1 provides the best accuracy withK452 iteration epochs and MSE is 0.00001536

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“…Machine learning and Deep learning approaches like -ANN, SVR, ELM, RF, SVM, (Zhang et al, 2020), k-NN, HybridNNs-ARIMAX with Exogeneous Variables (Anggraeni et al, 2019), deep-learning techniques like TDNN and LSTM network (Manogna 2020;Sabu & Kumar, 2020). However, studies with the application of deep learning in the tasks of agricultural price forecast are very scarce.…”

Section: Introductionmentioning

confidence: 99%

Deep Learning Based Models: Basic LSTM, Bi LSTM, Stacked LSTM, CNN LSTM and Conv LSTM to Forecast Agricultural Commodities Prices

Murugesan

Mishra

2021

Preprint

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The literature argues that an accurate price prediction of agricultural goods is a quintessence to assure a good functioning of the economy all over the world. Research reveals that studies with application of deep learning in the tasks of agricultural price forecast on short historical agricultural prices data are very scarce and insist on the use of different methods of deep learning to predict and to this reaction of filling the gap, this study employs five versions of LSTM deep learning techniques for the task of five agricultural commodities prices prediction on univariate time series dataset of Rice, Wheat, Gram, Banana, and Groundnut spanning January 2000 to July 2020. The study obtained good forecasting results for all five commodities employing all the five LSTM models. The study validated the results with lower values of error metrics, MAE, MAPE, MSE, and RMSE and two paired t-test with hypothesis and confidence level of 95% as a measure of robustness. The study predicted the one month ahead future price for all the five commodities and compared it with actual prices using said LSTM models and obtained promising results.

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Forecasting the Price of Indonesia’s Rice Using Hybrid Artificial Neural Network and Autoregressive Integrated Moving Average (Hybrid NNs-ARIMAX) with Exogenous Variables

Cited by 17 publications

References 9 publications

Forecasting Model of Staple Food Prices Using Support Vector Regression with Optimized Parameters

Forecasting Model of Staple Food Prices Using Support Vector Regression with Optimized Parameters

Determining the Best Performance Using the Backpropagation Algorithm for Expenditure per Capita in North Sumatra

Deep Learning Based Models: Basic LSTM, Bi LSTM, Stacked LSTM, CNN LSTM and Conv LSTM to Forecast Agricultural Commodities Prices

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