Hybrid ARIMA and Neural Network Modelling Applied to Telecommunications in Urban Environments in the Amazon Region

Lopes, Ramz L. Fraiha; Fraiha, Simone G. C.; Lima, Vinicius de Melo; Gomes, Cristiane Ruiz; Cavalcante, Gervásio P. S.

doi:10.1155/2020/2671746

Cited by 5 publications

(2 citation statements)

References 13 publications

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“…Electric field strengths were obtained through drive-test measurements, and a new generalized regression neural network model was created to predict power loss. In [26], a hybrid autoregressive integrated moving average and neural network model were used to predict electric field levels along with the buildings close to digital television transmitters. Computed and measured received signal strengths were compared, which were shown to be reasonably close.…”

Section: Related Workmentioning

confidence: 99%

Artificial Neural Network Based Prediction of Long-Term Electric Field Strength Level Emitted by 2G/3G/4G Base Station

Engiz

2023

Applied Sciences

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Accurate predictions of radio frequency electromagnetic field (RF-EMF) levels can help implement measures to reduce exposure and check regulatory compliance. Therefore, this study aims to predict the RF-EMF levels in the medium using an artificial neural network (ANN). The work was conducted at Ondokuz Mayis University, Kurupelit Campus, where the measurement location has line-of-sight to the base stations. Band selective measurements were also performed to assess the contribution of 2G/3G/4G services to the total RF-EMF level, which was found to be the highest among all services within the total band. Long-term RF-EMF measurements were carried out for 35 days within the frequencies of 100 kHz to 3 GHz. Then, an ANN model with Levenberg–Marquardt (LM) and Bayesian Regulation (BR) algorithms was proposed, which utilized inputs from real-time RF-EMF measurements. The performance of the models was assessed in terms of mean squared error (MSE) and regression performance. The average MSE and regression performances of the models were similar, with the lowest testing MSEs of 2.78 × 10−3 and 3.76 × 10−3 for LM and BR methods, respectively. The analysis of the models showed that the proposed models help to predict the RF-EMF level in the medium with up to 99% accuracy.

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Section: Related Workmentioning

confidence: 99%

Artificial Neural Network Based Prediction of Long-Term Electric Field Strength Level Emitted by 2G/3G/4G Base Station

Engiz

2023

Applied Sciences

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“…This economic phenomenon is of particular importance in developing economies like Sri Lanka, where the country's financial stability and growth trajectory are intricately linked to inflation trends [1]. Sri Lanka's unique economic landscape, marked by its transitional economy, presents a compelling case for detailed study, given the significant role inflation plays in both macroeconomic and microeconomic spheres [2].…”

Section: Introductionmentioning

confidence: 99%

Evaluating the Predictive Performance of Monthly Inflation Rates in Sri Lanka using the Hybrid Model (HB)

Bandara,

Mel

2023

AJPAS

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Aims/ objectives: This study develops and evaluates a novel hybrid model (HB) for forecasting monthly inflation rates in Sri Lanka, a country with a unique economic context, from 1988 to 2021. By integrating the Autoregressive Integrated Moving Average (ARIMA) and Artificial Neural Networks (ANNs), the study aims to overcome the limitations of traditional linear models in capturing the nonlinear patterns often observed in Sri Lankan economic data. Objectives: The study aims to assess the predictive accuracy of the HB model against established models, emphasizing its adaptability and robustness over a historically significant period. Methodology: Utilizing historical data, the study compares the HB model's forecasting performance with other established models, focusing on the Mean Absolute Percentage Error (MAPE) as a key metric of predictive accuracy. Results: The HB model demonstrates superior forecasting accuracy, with a notable reduction in MAPE to 7.10%, indicating its effectiveness in capturing the complexities of the Sri Lankan inflation trend. Conclusion: This study contributes to the field of economic forecasting by presenting a model that not only provides more accurate predictions but also adapts to the specific economic conditions of Sri Lanka. The findings have significant implications for economic planning and policy-making, highlighting the utility of hybrid forecasting models in developing economies.

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Forecast Methods for Time Series Data: A Survey

et al. 2021

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Research on forecasting methods of time series data has become one of the hot spots. More and more time series data are produced in various fields. It provides data for the research of time series analysis method, and promotes the development of time series research. Due to the generation of highly complex and large-scale time series data, the construction of forecasting models for time series data brings greater challenges. The main challenges of time series modeling are high complexity of time series data, low accuracy and poor generalization ability of prediction model. This paper attempts to cover the existing modeling methods for time series data and classify them. In addition, we make comparisons between different methods and list some potential directions for time series forecasting.

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Hybrid ARIMA and Neural Network Modelling Applied to Telecommunications in Urban Environments in the Amazon Region

Cited by 5 publications

References 13 publications

Artificial Neural Network Based Prediction of Long-Term Electric Field Strength Level Emitted by 2G/3G/4G Base Station

Artificial Neural Network Based Prediction of Long-Term Electric Field Strength Level Emitted by 2G/3G/4G Base Station

Evaluating the Predictive Performance of Monthly Inflation Rates in Sri Lanka using the Hybrid Model (HB)

Forecast Methods for Time Series Data: A Survey

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