Comparison of Indonesian Imports Forecasting by Limited Period Using SARIMA Method

Rosyid, Harits Ar; Aniendya, Mutyara Whening; Herwanto, Heru Wahyu

doi:10.17977/um018v2i22019p90-100

Cited by 6 publications

(4 citation statements)

References 13 publications

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“…Mean Absolute Percentage Error (MAPE) and Root Mean Square Error (RMSE) is the error detection used. MAPE is used for error detection, which represents accuracy [48], while RMSE is used for error detection based on outliers [49]. MAPE and RMSE values getting smaller and closer to 0 indicate a more accurate prediction result.…”

Section: Discussionmentioning

confidence: 99%

Bank Soundness Level Prediction: ANFIS vs Deep Learning

Maharani

2023

J. Appl. Data Sci.

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Financial systems depend on financial institution stability. This study used an ANFIS-ANN model to forecast bank insolvency probability. ANFIS, an adaptive network that combines fuzzy logic with ANN, is a promising bankruptcy prediction tool. This study compared the predictive performance of ANFIS against Long Short-Term Memory (LSTM) and Convolutional Neural Network (CNN) using financial statements data from 42 banking issuers listed on the Indonesia Stock Exchange (IDX) from 2010 to 2021. This work aimed to build a robust bankruptcy prediction model, compared ANFIS to LSTM and CNN, and assessed each method's accuracy and applicability. MAPE and RMSE metrics were used for a thorough review. The study collected, preprocessed, and trained ANFIS, LSTM, and CNN models. ANFIS had 243 rules with triangular membership functions for input variables and constant output membership. Random search optimized LSTM and CNN hyperparameters. This study's novelty lies in its innovative comparison of ANFIS and deep learning for bank bankruptcy prediction used real-world financial data. ANFIS beat LSTM and CNN in MAPE, showing its predictive model capability. ANFIS has the lowest MAPE of 0.140335507 when trained and tested on 80%:20% data. In conclusion, ANFIS outperformed deep learning in bank soundness prediction. It provided vital insights into the banking sector's early warning and risk assessment. The results showed that ANFIS can help financial decision-makers to improve risk management.

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Section: Discussionmentioning

confidence: 99%

Bank Soundness Level Prediction: ANFIS vs Deep Learning

Maharani

2023

J. Appl. Data Sci.

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“…If the data is not stationary in the mean, then a differencing process will be carried out. The number of differences determines the value of I (integrated) with the orders (d) and (D) in the model (Rosyid, Aniendya, Herwanto, & Shi, 2019).…”

Section: Discussionmentioning

confidence: 99%

Forecasting prevalence of dengue hemorrhagic fever using ARIMA model in Sulawesi Tenggara Province, Indonesia

Mistawati

Yasnani

Lestari

2021

PHI

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Background: Dengue hemorrhagic fever occurs through the bite of Aedes mosquitoes, primarily Aedes aegypti, carrying dengue viruses. In recent decades, the risk increased dramatically, not only in the tropics but also in subtropical regions.Objective: This study aimed to determine the best model for forecasting dengue hemorrhagic fever prevalence in Sulawesi Tenggara, Indonesia.Method: This was a retrospective analytical study using secondary data from the Sulawesi Tenggara Provincial Health Office from 2014 to 2019. ARIMA model was used for data analysis.Results: ARIMA (0.1.1)(0.1.1)4 was selected as the best-suited model. Based on the forecast, there would be an increase in dengue hemorrhagic fever prevalence over the next two years, with a mean absolute percentage error value of 4.41%.Conclusion: Forecasting results indicated that the peaks of dengue hemorrhagic fever cases would be in March, July, and November, and the increase will occur in the same months each year. Also, forecasting results were very good. Public health practitioners can use this model to prevent and eradicate dengue hemorrhagic fever. The ARIMA model would also be useful for nursing practice in caring for patients with dengue fever in the future.

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“…SARIMA requires the series to be univariate and stationary [26], which in this study is tested with the augmented Dickey-Fuller (ADF) parametric test [27] done using the adfuller function in the statsmodels Python library. The test returns the p-value and the critical values at 1%, 5%, and 10% confidence intervals.…”

Section: Sarima Methodsmentioning

confidence: 99%

Benchmark of Holt-Winters and SARIMA Methods in Predicting Jakarta Climate

Natayu¹,

Clarke²,

Fikri³

2022

Preprint

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As its capital, Jakarta plays a critical role in boosting Indonesia’s economic growth and setting the precedent for broader change outside of the city. One crucial avenue of inquiry to better understand, and prepare for, the future of a country so heavily impacted by disastrous weather events is understanding the effects of climate change through data. This study investigates meteorological data collected from 1996 to 2021 and compares the application of the SARIMA and the Holt-Winters methods to predict the future influence of climatic parameters on Jakarta’s weather. The performance of the SARIMA method is proven to provide better results than the Holt-Winter models and both methods showed the best performances when forecasting the humidity data. The results of the forecast are able to demonstrate the characteristic of the climate in Jakarta, with dry season ranging from May to October and wet season ranging from November to April.

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Comparison of Indonesian Imports Forecasting by Limited Period Using SARIMA Method

Cited by 6 publications

References 13 publications

Bank Soundness Level Prediction: ANFIS vs Deep Learning

Bank Soundness Level Prediction: ANFIS vs Deep Learning

Forecasting prevalence of dengue hemorrhagic fever using ARIMA model in Sulawesi Tenggara Province, Indonesia

Benchmark of Holt-Winters and SARIMA Methods in Predicting Jakarta Climate

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