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Journal articles on the topic 'NNAR'

Author: Grafiati

Published: 2 June 2025

Last updated: 31 July 2025

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1

Akermi, Seif Eddine, Mohamed L’Hadj, and Schehrazad Selmane. "Epidemiology and Time Series Analysis of Human Brucellosis in Tebessa Province, Algeria, from 2000 to 2020." Journal of Research in Health Sciences 22, no. 1 (2021): e00544-e00544. http://dx.doi.org/10.34172/jrhs.2022.79.

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Background: Brucellosis runs rampant endemically with sporadic outbreaks in Algeria. The present study aimed to provide insights into the epidemiology of brucellosis and compare the performance of some prediction models using surveillance data from Tebessa province, Algeria. Study Design: A retrospective study. Methods: Seasonal autoregressive integrated moving average (SARIMA), neural network autoregressive (NNAR), and hybrid SARIMA-NNAR models were developed to predict monthly brucellosis notifications. The prediction performance of these models was compared using root mean square error (RMS

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Komal Batool, Mirza Faizan Ahmed, and Muhammad Ali Ismail. "A Hybrid Model of Machine Learning Model and Econometrics’ Model to Predict Volatility of KSE-100 Index." Reviews of Management Sciences 4, no. 1 (2022): 225–39. http://dx.doi.org/10.53909/rms.04.01.0125.

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Purpose: The purpose of this paper is to predict the volatility of the KSE-100 index using econometric and machine learning models. It also designs hybrid models for volatility forecasting by combining these two models in three different ways. Methodology: Estimations and forecasting are based on an econometric model GARCH (Generalized Auto Regressive Conditional Heteroscedasticity) and a machine learning model NNAR (Neural Network Auto-Regressive model). The hybrid models designed with GARCH and NNAR include GARCH-based NNAR, NNAR-based GARCH, and the linear combination of GARCH and NNAR. Fin

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Sivhugwana, Khathutshelo Steven, and Edmore Ranganai. "Short-Term Wind Speed Prediction via Sample Entropy: A Hybridisation Approach against Gradient Disappearance and Explosion." Computation 12, no. 8 (2024): 163. http://dx.doi.org/10.3390/computation12080163.

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High-variant wind speeds cause aberrations in wind power systems and compromise the effective operation of wind farms. A single model cannot capture the inherent wind speed randomness and complexity. In the proposed hybrid strategy, wavelet transform (WT) is used for data decomposition, sample entropy (SampEn) for subseries complexity evaluation, neural network autoregression (NNAR) for deterministic subseries prediction, long short-term memory network (LSTM) for complex subseries prediction, and gradient boosting machine (GBM) for prediction reconciliation. The proposed WT-NNAR-LSTM-GBM appro

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Sianturi, Sri Novi Elizabeth, Betty Subartini, and Sukono Sukono. "Comparison of Stock Mutual Fund Price Forecasting Results Using ARIMA and Neural Network Autoregressive Model." International Journal of Quantitative Research and Modeling 6, no. 2 (2025): 208–17. https://doi.org/10.46336/ijqrm.v6i2.1001.

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Stock mutual funds gained popularity among the public as an investment alternative due to the convenience they offer, especially for beginner investors who have limited time and investment knowledge. Compared to money market and bond mutual funds, these mutual funds offer higher potential returns but also come with higher risks due to value fluctuations, so forecasting stock mutual fund prices is essential to minimize losses. Since stock mutual fund prices is time series data, this research employs two forecasting models such as Autoregressive Integrated Moving Average (ARIMA) and Neural Netwo

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Munim, Ziaul Haque, Mohammad Hassan Shakil, and Ilan Alon. "Next-Day Bitcoin Price Forecast." Journal of Risk and Financial Management 12, no. 2 (2019): 103. http://dx.doi.org/10.3390/jrfm12020103.

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This study analyzes forecasts of Bitcoin price using the autoregressive integrated moving average (ARIMA) and neural network autoregression (NNAR) models. Employing the static forecast approach, we forecast next-day Bitcoin price both with and without re-estimation of the forecast model for each step. For cross-validation of forecast results, we consider two different training and test samples. In the first training-sample, NNAR performs better than ARIMA, while ARIMA outperforms NNAR in the second training-sample. Additionally, ARIMA with model re-estimation at each step outperforms NNAR in t

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Zenia, Safia, Mohamed L’Hadj, and Schehrazad Selmane. "A Hybrid Approach Based on Seasonal Autoregressive Integrated Moving Average and Neural Network Autoregressive Models to Predict Scorpion Sting Incidence in El Oued Province, Algeria, From 2005 to 2020." Journal of Research in Health Sciences 23, no. 3 (2023): e00586. http://dx.doi.org/10.34172/jrhs.2023.121.

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Background: This study was designed to find the best statistical approach to scorpion sting predictions. Study Design: A retrospective study. Methods: Multiple regression, seasonal autoregressive integrated moving average (SARIMA), neural network autoregressive (NNAR), and hybrid SARIMA-NNAR models were developed to predict monthly scorpion sting cases in El Oued province. The root mean square error (RMSE), mean absolute error (MAE), and mean absolute percentage error (MAPE) were used to quantitatively compare different models. Results: In general, 96909 scorpion stings were recorded in El Oue

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Ordoñez Mercado, Alipio Francisco. "Modelos híbridos SARIMA-ANN para pronósticos de la COVID-19 en el Perú." Revista IECOS 22, no. 1 (2021): 7–22. http://dx.doi.org/10.21754/iecos.v22i1.1332.

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Se ha construido modelos híbridos ANN-ARIMA por remodelamiento, para realizar los pronósticos de los nuevos casos de contagios por Covid-19 en el Perú, para ello se extrajo y uso los casos confirmados de Covid-19 entre el periodo 06/03/20 hasta el 28/02/21, desde la plataforma de los datos abiertos del Ministerio de Salud. Los resultados hallados indican que los 02 mejores modelos corresponden al modelo hibrido multiplicativo NNAR (27,1,6) * ARIMA(3,0,2)(1,0,1), y al modelo hibrido aditivo NNAR (27,1,6) + ARIMA(1,0,1), cuyos valores del error medio absoluto porcentual(MAPE) se diferencian en t

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Yadav, Baikunth Kumar, Sunil Kumar Srivastava, Ponnusamy Thillai Arasu, and Pranveer Singh. "Time Series Modeling of Tuberculosis Cases in India from 2017 to 2022 Based on the SARIMA-NNAR Hybrid Model." Canadian Journal of Infectious Diseases and Medical Microbiology 2023 (December 16, 2023): 1–9. http://dx.doi.org/10.1155/2023/5934552.

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Tuberculosis (TB) is still one of the severe progressive threats in developing countries. There are some limitations to social and economic development among developing nations. The present study forecasts the notified prevalence of TB based on seasonality and trend by applying the SARIMA-NNAR hybrid model. The NIKSHAY database repository provides monthly informed TB cases (2017 to 2022) in India. A time series model was constructed based on the seasonal autoregressive integrated moving averages (SARIMA), neural network autoregressive (NNAR), and, SARIM-NNAR hybrid models. These models were es

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Sivhugwana, K. S., and E. Ranganai. "Intelligent techniques, harmonically coupled and SARIMA models in forecasting solar radiation data: A hybridization approach." Journal of Energy in Southern Africa 31, no. 3 (2020): 14–37. http://dx.doi.org/10.17159/2413-3051/2020/v31i3a7754.

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The unsteady and intermittent feature (mainly due to atmospheric mechanisms and diurnal cycles) of solar energy resource is often a stumbling block, due to its unpredictable nature, to receiving high-intensity levels of solar radiation at ground level. Hence, there has been a growing demand for accurate solar irradiance forecasts that properly explain the mixture of deterministic and stochastic characteristic (which may be linear or nonlinear) in which solar radiation presents itself on the earth’s surface. The seasonal autoregressive integrated moving average (SARIMA) models are popular for a

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Lam, Ka Chi, and Olalekan Shamsideen Oshodi. "Forecasting construction output: a comparison of artificial neural network and Box-Jenkins model." Engineering, Construction and Architectural Management 23, no. 3 (2016): 302–22. http://dx.doi.org/10.1108/ecam-05-2015-0080.

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Purpose – Fluctuations in construction output has an adverse effect on the construction industry and the economy due to its strong linkage. Developing reliable and accurate predictive models is vital to implementing effective response strategies to mitigate the impact of such fluctuations. The purpose of this paper is to compare the accuracy of two univariate forecast models, i.e. Box-Jenkins (autoregressive integrated moving average (ARIMA)) and Neural Network Autoregressive (NNAR). Design/methodology/approach – Four quarterly time-series data on the construction output of Hong Kong were coll

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Yu, Gongchao, Huifen Feng, Shuang Feng, Jing Zhao, and Jing Xu. "Forecasting hand-foot-and-mouth disease cases using wavelet-based SARIMA–NNAR hybrid model." PLOS ONE 16, no. 2 (2021): e0246673. http://dx.doi.org/10.1371/journal.pone.0246673.

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Background Hand-foot-and-mouth disease_(HFMD) is one of the most typical diseases in children that is associated with high morbidity. Reliable forecasting is crucial for prevention and control. Recently, hybrid models have become popular, and wavelet analysis has been widely performed. Better prediction accuracy may be achieved using wavelet-based hybrid models. Thus, our aim is to forecast number of HFMD cases with wavelet-based hybrid models. Materials and methods We fitted a wavelet-based seasonal autoregressive integrated moving average (SARIMA)–neural network nonlinear autoregressive (NNA

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As'ad, Mohamad, Sujito Sujito, and Sigit Setyowibowo. "Neural Network Autoregressive For Predicting Daily Gold Price." Jurnal INFORM 5, no. 2 (2020): 69. http://dx.doi.org/10.25139/inform.v0i1.2715.

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Gold is a precious metal that functions as a gem and also an investment. Gold investment is the reason for many people because it is practical, not easily damaged, easy cashed, not taxable, and other purposes. Based on this, many people choose gold as an investment. The problem for people who will invest in gold is related to uncertain gold price predictions so that the accuracy of forecasting methods are needed. The purpose of this paper is to forecast accurately daily gold prices using the Neural Network Autoregressive (NNAR) method. Training Data to find out the value of accuracy in the NNA

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Prapcoyo, Hari, and Mohamad As'ad. "Model Neural Network Autoregressive untuk Prediksi Inflasi Bulanan di Kota Yogyakarta." Jurnal Sistem dan Teknologi Informasi (JustIN) 11, no. 2 (2023): 213. http://dx.doi.org/10.26418/justin.v11i2.54370.

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AbstrakYogyakarta sebagai kota pelajar, kota pariwisata ataupun kota budaya sangatlah ramai aktifitas ekonominya karena banyak sekolah, universitas, tempat wisata dan juga tempat budaya yang tentunya banyak mahasiswa, wisatawan dalam negeri maupun luar negeri yang berkunjung ke kota tersebut. Aktifitas mahasiswa dan wisatawan di kota Yogyakarta ini bisa meningkatkan aktifitas perekonomian seperti tempat kost, penginapan atau hotel serta tidak ketinggalan tempat makan, tempat belanja dan lain sebagainya. Penelitian ini mempunyai tujuan untuk memprediksi inflasi bulanan di kota Yogyakarta yang r

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Melina, Melina, Sukono Sukono, Herlina Napitupulu, et al. "COMPARATIVE ANALYSIS OF TIME SERIES FORECASTING MODELS USING ARIMA AND NEURAL NETWORK AUTOREGRESSION METHODS." BAREKENG: Jurnal Ilmu Matematika dan Terapan 18, no. 4 (2024): 2563–76. http://dx.doi.org/10.30598/barekengvol18iss4pp2563-2576.

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Gold price fluctuations have a significant impact because gold is a haven asset. When financial markets are volatile, investors tend to turn to safer instruments such as gold, so gold price forecasting becomes important in economic uncertainty. The novelty of this research is the comparative analysis of time series forecasting models using ARIMA and the NNAR methods to predict gold price movements specifically applied to gold price data with non-stationary and non-linear characteristics. The aim is to identify the strengths and limitations of ARIMA and NNAR on such data. ARIMA can only be appl

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Daniyal, Muhammad, Kassim Tawiah, Sara Muhammadullah, and Kwaku Opoku-Ameyaw. "Comparison of Conventional Modeling Techniques with the Neural Network Autoregressive Model (NNAR): Application to COVID-19 Data." Journal of Healthcare Engineering 2022 (June 14, 2022): 1–9. http://dx.doi.org/10.1155/2022/4802743.

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The coronavirus disease 2019 (COVID-19) pandemic continues to destroy human life around the world. Almost every country throughout the globe suffered from this pandemic, forcing various governments to apply different restrictions to reduce its impact. In this study, we compare different time-series models with the neural network autoregressive model (NNAR). The study used COVID-19 data in Pakistan from February 26, 2020, to February 18, 2022, as a training and testing data set for modeling. Different models were applied and estimated on the training data set, and these models were assessed on

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Puoetsile, Agolame, Mokaedi Lekgari, Semu Kassa, and Gizaw Mengistu Tsidu. "Optimized Parameter Estimation and Integrating Neural Network Forecasting of Dynamic Plant-Livestock Model for Early Warning in Agro-Environment Control Systems." Statistics, Optimization & Information Computing 12, no. 5 (2024): 1460–75. http://dx.doi.org/10.19139/soic-2310-5070-1906.

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The research utilizes the Lotka-Volterra prey-predator model to study Plant-Herbivore dynamics, focusing on the relationship between traditional livestock farming and vegetation conditions. Advanced methods are developed to improve the precision and efficiency of parameter estimation in these models. Neural networks are incorporated to enhance forecasting abilities, and an extension of the Plant-Herbivore models includes Botswana's climate and livestock variables. Efficient parameter space exploration is achieved using the Runge-Kutta method along with Multistart and the local solver $fmincon$

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As'ad, Mohamad, Sujito Sujito, and Sigit Setyowibowo. "Neural Network Autoregressive For Predicting Daily Gold Price." Inform : Jurnal Ilmiah Bidang Teknologi Informasi dan Komunikasi 5, no. 2 (2020): 69–73. http://dx.doi.org/10.25139/inform.v5i2.2715.

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Emas adalah logam mulia yang dapat berfungsi sebagai permata dan juga investasi. Sebagai investasi emas memang praktis karena tidak mudah rusak, mudah diuangkan, tidak kena pajak dan alasan yang lainnya. Sebagai investasi, emas mudah diuangkan ketika dibutuhkan, sehingga banyak masyarakat yang memilih emas sebagai investasi. Supaya berivestasi emas tidak rugi, maka diperlukan perkiraan harga emas saat membeli dan menjual. Banyak metode yang bisa dipakai dalam memprediksi harga emas harian, baik secara statistika maupun secara intelegensi buatan. Pada penelitian ini data yang digunakan adalah d

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Shokeralla, Dr Alshaikh A. "A Comparative Analysis of NNAR and LSTM Models for Short-Term COVID-19 Forecasting in Saudi Arabia." International Journal of Soft Computing and Engineering 15, no. 2 (2025): 31–39. https://doi.org/10.35940/ijsce.b3657.15020525.

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The COVID-19 pandemic has posed an ongoing challenge for public health systems around the globe. Accurate forecasting of daily confirmed COVID-19 cases in Saudi Arabia has remained critical for informed planning and timely interventions. This research explores and compares the predictive performance of two artificial neural network models—Nonlinear Autoregressive Neural Network (NNAR) and Long Short-Term Memory (LSTM)—applied to Saudi Arabia’s COVID-19 case data from March 2020 through December 2021. Using standard evaluation metrics, including MAE, RMSE, MAPE, and Theil’s U, the study demonst

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Lima, Patricia Virginia de Santana, David Venâncio da Cruz, and Albaro Ramon Paiva Sanz. "Predicting bitcoin cryptocurrency price behavior based on ARIMA and NNAR modelling." Socioeconomic Analytics 2, no. 1 (2024): 121–29. https://doi.org/10.51359/2965-4661.2024.265073.

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The development of models to predict the behavior of the Bitcoin cryptocurrency, using a public database (Yahoo! Finance) to predict price trends. The models used were ARIMA and NNAR with the validation of the models being carried out based on the daily closing values of the asset. Both models did not differ significantly, however the adjusted model NNAR (2.2) had a slightly better fit to the original data series, presenting an MPE (Mean Percentage Error) of -0.102.

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Dr., Alshaikh A. Shokeralla. "A Comparative Analysis of NNAR and LSTM Models for Short-Term COVID-19 Forecasting in Saudi Arabia." International Journal of Soft Computing and Engineering (IJSCE) 15, no. 2 (2025): 31–39. https://doi.org/10.35940/ijsce.B3657.15020525.

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<strong>Abstract: </strong>The COVID-19 pandemic has posed an ongoing challenge for public health systems around the globe. Accurate forecasting of daily confirmed COVID-19 cases in Saudi Arabia has remained critical for informed planning and timely interventions. This research explores and compares the predictive performance of two artificial neural network models—Nonlinear Autoregressive Neural Network (NNAR) and Long Short-Term Memory (LSTM)—applied to Saudi Arabia’s COVID-19 case data from March 2020 through December 2021. Using standard evaluation metrics, including MAE,

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Dr., Alshaikh A. Shokeralla. "A Comparative Analysis of NNAR and LSTM Models for Short-Term COVID-19 Forecasting in Saudi Arabia." International Journal of Soft Computing and Engineering (IJSCE) 15, no. 2 (2025): 31–39. https://doi.org/10.35940/ijsce.B3657.15020525/.

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<strong>Abstract: </strong>The COVID-19 pandemic has posed an ongoing challenge for public health systems around the globe. Accurate forecasting of daily confirmed COVID-19 cases in Saudi Arabia has remained critical for informed planning and timely interventions. This research explores and compares the predictive performance of two artificial neural network models—Nonlinear Autoregressive Neural Network (NNAR) and Long Short-Term Memory (LSTM)—applied to Saudi Arabia’s COVID-19 case data from March 2020 through December 2021. Using standard evaluation metrics, including MAE,

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Adi Pratama, Ihram, and Anneke Iswani Achmad. "Perbandingan Holt-Winters Exponential Smoothing dengan Artificial Neural Networks dalam Peramalan Produksi Cabai Besar di Kabupaten Garut Provinsi Jawa Barat." Bandung Conference Series: Statistics 3, no. 2 (2023): 246–56. http://dx.doi.org/10.29313/bcss.v3i2.7900.

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Abstract. This study discusses the comparison of Holt-Winters Exponential Smoothing method with Artificial Neural Networks in forecasting large chili production, Garut Regency, West Java Province. The Holt-Winters Exponential Smoothing method is a forecasting method on time series data with trend and seasonal data patterns based on three equations, one for stationary, one for trend and one for seasonal. There are two methods in Holt-Winters Exponential Smoothing, namely multiplicative and additive.Artificial Neural Networks is a method inspired by human central neural networks. One method that

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Ahmar, Ansari Saleh, and Eva Boj. "Application of Neural Network Time Series (NNAR) and ARIMA to Forecast Infection Fatality Rate (IFR) of COVID-19 in Brazil." JOIV : International Journal on Informatics Visualization 5, no. 1 (2021): 8. http://dx.doi.org/10.30630/joiv.5.1.372.

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Forecasting is a method that is often used to view future events using past time data. Past time data have useful information to use in obtaining the future. The aim of this study was to forecast infection fatality rate (IFR) of COVID-19 in Brazil using NNAR and ARIMA. ARIMA and NNAR are used because (1) ARIMA is a simple stochastic time series method that can be used to train and predict future time points and ARIMA also capable of capturing dynamic interactions when it uses error terms and observations of lagged terms; (2) the Artificial Neural Network (ANN) is a technique capable of analyzi

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Davidescu, Adriana AnaMaria, Simona-Andreea Apostu, and Andreea Paul. "Comparative Analysis of Different Univariate Forecasting Methods in Modelling and Predicting the Romanian Unemployment Rate for the Period 2021–2022." Entropy 23, no. 3 (2021): 325. http://dx.doi.org/10.3390/e23030325.

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Unemployment has risen as the economy has shrunk. The coronavirus crisis has affected many sectors in Romania, some companies diminishing or even ceasing their activity. Making forecasts of the unemployment rate has a fundamental impact and importance on future social policy strategies. The aim of the paper is to comparatively analyze the forecast performances of different univariate time series methods with the purpose of providing future predictions of unemployment rate. In order to do that, several forecasting models (seasonal model autoregressive integrated moving average (SARIMA), self-ex

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Punyapornwithaya, Veerasak, Pradeep Mishra, Chalutwan Sansamur, et al. "Time-Series Analysis for the Number of Foot and Mouth Disease Outbreak Episodes in Cattle Farms in Thailand Using Data from 2010–2020." Viruses 14, no. 7 (2022): 1367. http://dx.doi.org/10.3390/v14071367.

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Thailand is one of the countries where foot and mouth disease outbreaks have resulted in considerable economic losses. Forecasting is an important warning technique that can allow authorities to establish an FMD surveillance and control program. This study aimed to model and forecast the monthly number of FMD outbreak episodes (n-FMD episodes) in Thailand using the time-series methods, including seasonal autoregressive integrated moving average (SARIMA), error trend seasonality (ETS), neural network autoregression (NNAR), and Trigonometric Exponential smoothing state–space model with Box–Cox t

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Abderrahmane, Ballah, Morad Chahid, Mourad Aqnouy, Adam M. Milewski, and Benaabidate Lahcen. "Evaluating Time Series Models for Monthly Rainfall Forecasting in Arid Regions: Insights from Tamanghasset (1953–2021), Southern Algeria." Geosciences 15, no. 7 (2025): 273. https://doi.org/10.3390/geosciences15070273.

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Accurate precipitation forecasting remains a critical challenge due to the nonlinear and multifactorial nature of rainfall dynamics. This is particularly important in arid regions like Tamanghasset, where precipitation is the primary driver of agricultural viability and water resource management. This study evaluates the performance of several time series models for monthly rainfall prediction, including the autoregressive integrated moving average (ARIMA), Exponential Smoothing State Space Model (ETS), Seasonal and Trend decomposition using Loess with ETS (STL-ETS), Trigonometric Box–Cox tran

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Ahmar, Ansari Saleh, Pawan Kumar Singh, R. Ruliana, Alok Kumar Pandey, and Stuti Gupta. "Comparison of ARIMA, SutteARIMA, and Holt-Winters, and NNAR Models to Predict Food Grain in India." Forecasting 5, no. 1 (2023): 138–52. http://dx.doi.org/10.3390/forecast5010006.

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The agriculture sector plays an essential function within the Indian economic system. Foodgrains provide almost all the calories and proteins. This paper aims to compare ARIMA, SutteARIMA, Holt-Winters, and NNAR models to recommend an effective model to predict foodgrains production in India. The execution of the SutteARIMA predictive model used in this analysis was compared with the established ARIMA, Neural Network Auto-Regressive (NNAR), and Holt-Winters models, which have been widely applied for time series prediction. The findings of this study reveal that both the SutteARIMA model and th

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Colak, Mehmet Berke, and Erkan Özhan. "Renewable Energy Forecasting in Turkey: Analytical Approaches." Journal of Intelligent Systems: Theory and Applications 8, no. 1 (2025): 25–34. https://doi.org/10.38016/jista.1447980.

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The growing population and industrialization have resulted in an increased demand for energy, which has worsened environmental problems such as pollution and climate change. Renewable energy sources are considered a promising solution due to their environmental benefits and limited potential. This study examines the use of neural networks and time series analysis to predict electricity generation rates from renewable energy sources in Turkey. We use the LSTM, NNAR, and ELM models, all of which utilize the backpropagation algorithm for neural network forecasting. Additionally, we apply ARIMA, H

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Anwar, Ayesha, Kannika Na-Lampang, Narin Preyavichyapugdee, and Veerasak Punyapornwithaya. "Lumpy Skin Disease Outbreaks in Africa, Europe, and Asia (2005–2022): Multiple Change Point Analysis and Time Series Forecast." Viruses 14, no. 10 (2022): 2203. http://dx.doi.org/10.3390/v14102203.

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LSD is an important transboundary disease affecting the cattle industry worldwide. The objectives of this study were to determine trends and significant change points, and to forecast the number of LSD outbreak reports in Africa, Europe, and Asia. LSD outbreak report data (January 2005 to January 2022) from the World Organization for Animal Health were analyzed. We determined statistically significant change points in the data using binary segmentation, and forecast the number of LSD reports using auto-regressive moving average (ARIMA) and neural network auto-regressive (NNAR) models. Four sig

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KARABAY, GULSEREN, MUHAMMET BURAK KILIC, KAZIM SARICOBAN, and GİZEM KARAKAN GÜNAYDIN. "Forecasting of Turkey's apparel exports using artificial neural network autoregressive models." Industria Textila 74, no. 02 (2023): 143–53. http://dx.doi.org/10.35530/it.074.02.202265.

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Foreign trade is significant for open economies and has a critical place in the development of national economies in a globally competitive environment. Export has a key role as an important component in foreign trade transactions. In this study, Turkey's exports of HS-61 “Apparel and clothing accessories knitted or crocheted” and HS-62 “Apparel and clothing accessories not knitted or crocheted” products were examined. Turkey's exports of these HS codes to seven countries, which are mostly exported, EU27, OECD and the world were estimated for 2020–2025 using artificial neural networks (ANNs).

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Silva, Emmanuel, Hossein Hassani, Dag Madsen, and Liz Gee. "Googling Fashion: Forecasting Fashion Consumer Behaviour Using Google Trends." Social Sciences 8, no. 4 (2019): 111. http://dx.doi.org/10.3390/socsci8040111.

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This paper aims to discuss the current state of Google Trends as a useful tool for fashion consumer analytics, show the importance of being able to forecast fashion consumer trends and then presents a univariate forecast evaluation of fashion consumer Google Trends to motivate more academic research in this subject area. Using Burberry—a British luxury fashion house—as an example, we compare several parametric and nonparametric forecasting techniques to determine the best univariate forecasting model for “Burberry” Google Trends. In addition, we also introduce singular spectrum analysis as a u

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Almarashi, Abdullah M., Muhammad Daniyal, and Farrukh Jamal. "Modelling the GDP of KSA using linear and non-linear NNAR and hybrid stochastic time series models." PLOS ONE 19, no. 2 (2024): e0297180. http://dx.doi.org/10.1371/journal.pone.0297180.

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Background Gross domestic product (GDP) serves as a crucial economic indicator for measuring a country’s economic growth, exhibiting both linear and non-linear trends. This study aims to analyze and propose an efficient and accurate time series approach for modeling and forecasting the GDP annual growth rate (%) of Saudi Arabia, a key financial indicator of the country. Methodology Stochastic linear and non-linear time series modeling, along with hybrid approaches, are employed and their results are compared. Initially, conventional linear and nonlinear methods such as ARIMA, Exponential smoot

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Goud, R. Gautham, and Prof M. Krishna Reddy. "Forecasting of P/E Ratio for the Indian Equity Market Stock Index NIFTY 50 Using Neural Networks." International Journal of Management and Humanities 10, no. 5 (2024): 1–9. http://dx.doi.org/10.35940/ijmh.f1576.10050124.

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The ratio of present price of an index to its earnings is known as its price to earnings ratio denoted by P/E ratio. A high P/E means that an index’s price is high relative to earnings and overvalued. Its low value means that price is low relative to earnings and undervalued. A potential investor prefers an index with low P/E ratio. Therefore, the movement of the P/E ratio plays a crucial role in understanding the behaviour of the stock market. In this paper the modelling of the P/E ratio for the Indian equity market stock index NIFTY 50 using NNAR, MLP and ELM neural networks models and the t

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R, Gautham Goud. "Forecasting of P/E Ratio for the Indian Equity Market Stock Index NIFTY 50 Using Neural Networks." International Journal of Management and Humanities (IJMH) 10, no. 5 (2024): 1–9. https://doi.org/10.35940/ijmh.F1576.10050124.

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<strong>Abstract:</strong> The ratio of present price of an index to its earnings is known as its price to earnings ratio denoted by P/E ratio. A high P/E means that an index’s price is high relative to earnings and overvalued. Its low value means that price is low relative to earnings and undervalued. A potential investor prefers an index with low P/E ratio. Therefore, the movement of the P/E ratio plays a crucial role in understanding the behaviour of the stock market. In this paper the modelling of the P/E ratio for the Indian equity market stock index NIFTY 50 using NNAR, MLP and ELM

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Agbenyega, Diana Ayorkor, John Andoh, Samuel Iddi, and Louis Asiedu. "Modelling Customs Revenue in Ghana Using Novel Time Series Methods." Applied Computational Intelligence and Soft Computing 2022 (April 18, 2022): 1–8. http://dx.doi.org/10.1155/2022/2111587.

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Governments across the world rely on their Customs Administration to provide functions that include border security, intellectual property rights protection, environmental protection, and revenue mobilisation amongst others. Analyzing the trends in revenue being collected from Customs is necessary to direct government policies and decisions. Models that can capture the trends being purported from the nominal (nonreal) tax values with respect to the trade volumes (value) over the period are indispensable. Predominant amongst the existing models are the econometric models (the GDP-based model, t

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Junita, Tarisya Permata, and Mujiati Dwi Kartikasari. "APPLICATION OF THE NEURAL NETWORK AUTOREGRESSIVE (NNAR) METHOD FOR FORECASTING THE VALUE OF OIL AND GAS EXPORTS IN INDONESIA." BAREKENG: Jurnal Ilmu Matematika dan Terapan 18, no. 1 (2024): 0341–48. http://dx.doi.org/10.30598/barekengvol18iss1pp0341-0348.

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Indonesia is one of the countries with the most diversity and abundant natural resources, consisting of many commodities, and has enormous trade potential with other countries The success of economic activity a country can be measured by the amount of economic growth that occurs in the country. A recession is when a country's economic condition is getting worse. Meanwhile, a recession in Indonesia is expected to occur in 2023. In a 2022 news issue written by the editorial team, tirto.id said that some experts say that if 2023 is a recession, the cause is due to a spike in inflation from the im

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Mustaffa, Nurakmal Ahmad, Siti Mariam Zahari, Nor Alia Farhana, Noryanti Nasir, and Aishah Hani Azil. "Forecasting the incidence of dengue fever in Malaysia: A comparative analysis of seasonal ARIMA, dynamic harmonic regression, and neural network models." International Journal of ADVANCED AND APPLIED SCIENCES 11, no. 1 (2024): 20–31. http://dx.doi.org/10.21833/ijaas.2024.01.003.

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Currently, no vaccines or specific treatments are available to treat or prevent the increasing incidence of dengue worldwide. Therefore, an accurate prediction model is needed to support the anti-dengue control strategy. The primary objective of this study is to develop the most accurate model to predict future dengue cases in the Malaysian environment. This study uses secondary data collected from the weekly reports of the Ministry of Health Malaysia (MOH) website over six years, from 2017 to 2022. Three forecasting techniques, including seasonal autoregressive integrated moving average (SARI

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Sulandari, Winita, Yudho Yudhanto, and Paulo Canas Rodrigues. "The Use of Singular Spectrum Analysis and K-Means Clustering-Based Bootstrap to Improve Multistep Ahead Load Forecasting." Energies 15, no. 16 (2022): 5838. http://dx.doi.org/10.3390/en15165838.

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In general, studies on short-term hourly electricity load modeling and forecasting do not investigate in detail the sources of uncertainty in forecasting. This study aims to evaluate the impact and benefits of applying bootstrap aggregation in overcoming the uncertainty in time series forecasting, thereby increasing the accuracy of multistep ahead point forecasts. We implemented the existing and proposed clustering-based bootstrapping methods to generate new electricity load time series. In the proposed method, we use singular spectrum analysis to decompose the series between signal and noise

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Pontoh, Resa Septiani, Toni Toharudin, Budi Nurani Ruchjana, et al. "Jakarta Pandemic to Endemic Transition: Forecasting COVID-19 Using NNAR and LSTM." Applied Sciences 12, no. 12 (2022): 5771. http://dx.doi.org/10.3390/app12125771.

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In December 2021, the latest COVID-19 variant, Omicron, was confirmed in Indonesia. Unlike the Delta variant, the number of deaths in the Omicron type did not increase significantly and remained constant, even though the cases increased significantly. It is hoped that Indonesia will declare COVID-19 endemic. Jakarta is the capital of Indonesia and the first city where the new COVID-19 virus emerged. Therefore, we are trying to model COVID-19 cases in Jakarta and predict future cases to see if endemic conditions are identified. We applied Neural Network Auto-Regressive (NNAR) and Long Short-Ter

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Gunter, Ulrich. "Improving Hotel Room Demand Forecasts for Vienna across Hotel Classes and Forecast Horizons: Single Models and Combination Techniques Based on Encompassing Tests." Forecasting 3, no. 4 (2021): 884–919. http://dx.doi.org/10.3390/forecast3040054.

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The present study employs daily data made available by the STR SHARE Center covering the period from 1 January 2010 to 31 January 2020 for six Viennese hotel classes and their total. The forecast variable of interest is hotel room demand. As forecast models, (1) Seasonal Naïve, (2) Error Trend Seasonal (ETS), (3) Seasonal Autoregressive Integrated Moving Average (SARIMA), (4) Trigonometric Seasonality, Box–Cox Transformation, ARMA Errors, Trend and Seasonal Components (TBATS), (5) Seasonal Neural Network Autoregression (Seasonal NNAR), and (6) Seasonal NNAR with an external regressor (seasonal

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Fayomi, Aisha, Jamal Abdul Nasir, Ali Algarni, Muhammad Shoaib Rasool, Farrukh Jamal, and Christophe Chesneau. "Best selected forecasting models for COVID-19 pandemic." Open Physics 20, no. 1 (2022): 1303–12. http://dx.doi.org/10.1515/phys-2022-0218.

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Abstract This study sought to identify the most accurate forecasting models for COVID-19-confirmed cases, deaths, and recovered patients in Pakistan. For COVID-19, time series data are available from 16 April to 15 August 2021 from the Ministry of National Health Services Regulation and Coordination’s health advice portal. Descriptive as well as time series models, autoregressive integrated moving average, exponential smoothing models (Brown, Holt, and Winters), neural networks, and Error, Trend, Seasonal (ETS) models were applied. The analysis was carried out using the R coding language. The

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Parreño, Samuel John Estenor. "Forecasting Measles Incidence in the Philippines: A Comparative Analysis of SARIMA, Holt-Winters, ESN, and NNAR Models." Multidisciplinary Science Journal 7, no. 7 (2025): 2025356. https://doi.org/10.31893/multiscience.2025356.

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This study examines the incidence of measles in the Philippines from January 1, 2017, to October 28, 2023, employing four distinct forecasting models: Seasonal Autoregressive Integrated Moving Average (SARIMA), Holt-Winters Exponential Smoothing, Echo State Network (ESN), and Neural Network Autoregressive (NNAR). The primary objective is to determine the most effective method for predicting short-term measles incidence trends. Using officially released data on notifiable diseases, the study addresses challenges such as missing data through Predictive Mean Matching (PMM) and evaluates the perfo

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Tudor, Cristiana, and Robert Sova. "Benchmarking GHG Emissions Forecasting Models for Global Climate Policy." Electronics 10, no. 24 (2021): 3149. http://dx.doi.org/10.3390/electronics10243149.

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Climate change and pollution fighting have become prominent global concerns in the twenty-first century. In this context, accurate estimates for polluting emissions and their evolution are critical for robust policy-making processes and ultimately for solving stringent global climate challenges. As such, the primary objective of this study is to produce more accurate forecasts of greenhouse gas (GHG) emissions. This in turn contributes to the timely evaluation of the progress achieved towards meeting global climate goals set by international agendas and also acts as an early-warning system. We

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Parreño, Samuel John Estenor, and Maria Cristine Joy Anter. "New approach for forecasting rice and corn production in the Philippines through machine learning models." Multidisciplinary Science Journal 6, no. 9 (2024): 2024168. http://dx.doi.org/10.31893/multiscience.2024168.

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This study presents a comprehensive analysis of machine learning models for forecasting rice and corn production in the Philippines, focusing on determining the most effective model for this purpose. Given the crucial role of these crops in the nation's economy and food security, accurate forecasting is essential. We compared four different models: Random Forest (RF), Echo State Network (ESN), Neural Network Autoregressive (NNAR), and Autoregressive Support Vector Machine (ARSVM), using historical production data from 1987 to the first quarter of 2023. The Random Forest model, configured with

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Alsheheri, Ghadah. "Comparative Analysis of ARIMA and NNAR Models for Time Series Forecasting." Journal of Applied Mathematics and Physics 13, no. 01 (2025): 267–80. https://doi.org/10.4236/jamp.2025.131012.

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Bhattacharjee, S., K. Lekshmi, and R. Bharti. "TIME SERIES ANALYSIS OF URBANISATION IMPACT ON THE TEMPERATURE VARIATIONS OFF MUMBAI COAST." International Archives of the Photogrammetry, Remote Sensing and Spatial Information Sciences XLIII-B3-2021 (June 28, 2021): 31–37. http://dx.doi.org/10.5194/isprs-archives-xliii-b3-2021-31-2021.

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Abstract. Urbanisation is an ever-evolving, complicated continuous process distinct from its surroundings, having the tendency to create a micro-scale system with characteristic local environmental conditions. Large-scale urbanization near the coasts has a definite impact on the coastal processes due to dynamic interactions of the coastal waters with the urban atmospheric, hydrological and anthropogenic residues. This study focuses on understanding the contribution of immediate atmospheric variations due to urbanization on surface temperature of coastal waters along the Mumbai coast. Different

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Awariefe, C., and O. Ogbereyivwe. "Time Series Modelling and Forecasting Foreign Direct Investment using Linear and Nonlinear Models: The Case of Nigeria." Journal of Basics and Applied Sciences Research 2, no. 1 (2024): 46–53. http://dx.doi.org/10.33003/jobasr-2024-v2i1-33.

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This study addresses the crucial need for accurate forecasting of Foreign Direct Investment (FDIT) trends in Nigeria. FDIT plays a pivotal role in the country's economic growth and development efforts, driving industrialization, infrastructure enhancement, and job creation. However, predicting FDIT accurately is essential for policymakers, investors, and researchers to formulate effective strategies and decisions. This study conducts a comparative analysis of four FDIT forecasting models: Simple Exponential Smoothing (SES), Holt Exponential Smoothing (HES), ARIMA, and NNAR in Nigeria, utilizin

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Jainonthee, Chalita, Panneepa Sivapirunthep, Pranee Pirompud, Veerasak Punyapornwithaya, Supitchaya Srisawang, and Chanporn Chaosap. "Modeling and Forecasting Dead-on-Arrival in Broilers Using Time Series Methods: A Case Study from Thailand." Animals 15, no. 8 (2025): 1179. https://doi.org/10.3390/ani15081179.

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Antibiotic-free (ABF) broiler production plays an important role in promoting sustainable and welfare-oriented poultry farming. However, this production system presents challenges, particularly an increased susceptibility to stress and mortality during transport. This study aimed to (i) analyze time series data on the monthly percentage of dead-on-arrival (%DOA) and (ii) compare the performance of various time series models. Data on %DOA from 127,578 broiler transport truckloads recorded between 2018 and 2024 were aggregated into monthly %DOA values. The data were then decomposed to identify t

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Leite Coelho da Silva, Felipe, Kleyton da Costa, Paulo Canas Rodrigues, Rodrigo Salas, and Javier Linkolk López-Gonzales. "Statistical and Artificial Neural Networks Models for Electricity Consumption Forecasting in the Brazilian Industrial Sector." Energies 15, no. 2 (2022): 588. http://dx.doi.org/10.3390/en15020588.

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Forecasting the industry’s electricity consumption is essential for energy planning in a given country or region. Thus, this study aims to apply time-series forecasting models (statistical approach and artificial neural network approach) to the industrial electricity consumption in the Brazilian system. For the statistical approach, the Holt–Winters, SARIMA, Dynamic Linear Model, and TBATS (Trigonometric Box–Cox transform, ARMA errors, Trend, and Seasonal components) models were considered. For the approach of artificial neural networks, the NNAR (neural network autoregression) and MLP (multil

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Maleki, Afshin, Simin Nasseri, Mehri Solaimany Aminabad, and Mahdi Hadi. "Comparison of ARIMA and NNAR Models for Forecasting Water Treatment Plant’s Influent Characteristics." KSCE Journal of Civil Engineering 22, no. 9 (2018): 3233–45. http://dx.doi.org/10.1007/s12205-018-1195-z.

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