Predicting Fatigue-Associated Aberrant Driving Behaviors Using a Dynamic Weighted Moving Average Model With a Long Short-Term Memory Network Based on Heart Rate Variability

Tsai, Cheng-Yu; Cheong, He-in; Houghton, Robert; Lee, Kang‐Yun; Kang, Jiunn Horng; Kuan, Yi‐Chun; Lee, Hsin Chien; Wu, Chia-Chang; Li, Lok-Yee Joyce; Lin, Yi; Lin, Shang‐Yang; Manole, Iulia; Majumdar, Arnab; Liu, Wen‐Te

doi:10.1177/00187208231183874

Cited by 2 publications

(1 citation statement)

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“…Te MA model [15,16], or the moving average model, is a technique for forecasting time series data that involves utilizing a moving average of historical observations to anticipate future values. Te model assumes that the future values of a time series are a function of the average of the previous observations, with the weights of the observations determined by the time lag.…”

Section: Related Workmentioning

confidence: 99%

Optimal Gasoline Price Predictions: Leveraging the ANFIS Regression Model

Eliwa,

El Koshiry,

Abd El-Hafeez

et al. 2024

International Journal of Intelligent Systems

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This study presents an in-depth analysis of gasoline price forecasting using the adaptive network-based fuzzy inference system (ANFIS), with an emphasis on its implications for policy-making and strategic decisions in the energy sector. The model leverages a comprehensive dataset from the U.S. Energy Information Administration, spanning over 30 years of historical price data from 1993 to 2023, along with relevant temporal features. By combining the strengths of fuzzy logic and neural networks, the ANFIS approach can effectively capture the complex, nonlinear relationships present in the data, enabling reliable price predictions. The dataset’s preprocessing involved decomposing the date into year, month, and day components to enhance the model’s input features. Our methodology entailed a systematic approach to ANFIS regression, including data preparation, model training with the inclusion of the previous week’s prices as an additional feature, and rigorous performance evaluation using MSE, RMSE, and correlation coefficients. The results indicate that incorporating previous prices significantly enhances the model’s accuracy, as reflected by improved scores and correlation metrics. The findings have significant implications for the energy sector, where stakeholders can leverage the ANFIS model’s insights for strategic decision-making. Accurate gasoline price forecasts are instrumental in devising pricing strategies, managing risks associated with price volatility, and guiding policy formulation. The model’s predictive capability enables energy companies to optimize resource allocation, plan for future investments, and maintain competitive advantage in a market influenced by fluctuating prices. Moreover, policymakers can utilize these predictions to assess the impact of energy policies on market prices and consumer behavior, ensuring that regulatory measures align with market dynamics and sustainability goals. In addition to the ANFIS model, we also employed Vector Autoregression (VAR) and Autoregressive Integrated Moving Average (ARIMA) models to validate our approach and provide a comprehensive understanding of time series forecasting within the energy sector. Notably, the ANFIS model achieves a score of 0.9970 and a robust correlation of 0.9985, demonstrating its ability to accurately forecast gasoline prices based on historical data and features. The integration of these traditional techniques with advanced ANFIS modeling offers a robust framework for accurate and reliable gasoline price prediction, which is vital for informed policy-making and strategic planning in the energy industry.

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Section: Related Workmentioning

confidence: 99%