ARIMA forecasting of primary energy demand by fuel in Turkey
644 citations recorded by the source corpus.
Subfields: Economics and Econometrics, Electrical and Electronic Engineering, Renewable Energy, Sustainability and the Environment
View Paper: https://www.semanticscholar.org/paper/22a7d861e76ca542025ca0c2e957ca08556f1c0c
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Citations in the corpus
- Improvement of energy demand forecasts using swarm intelligence: The case of Turkey with projections to 2025 (2008, methodology): “Ediger and Akar (2007) used the autoregressive integrated moving average (ARIMA) and seasonal ARIMA (SARIMA) methods to estimate the future primary energy demand of Turkey from 2005 to 2020.”
- ARIMA Model for Gold Bullion Coin Selling Prices Forecasting (2012, methodology): “Ediger and Akar [5] used ARIMA model and seasonal ARIMA methods to forecast primary energy demand on fossil fuel in Turkey starts in year 2005 to year 2020.”
- Multivariate Temporal Convolutional Network: A Deep Neural Networks Approach for Multivariate Time Series Forecasting (2019, methodology): “The improved models, such as autoregressive integrated moving average (ARIMA) [15], autoregressive moving average (ARMA) [16], and vector auto-regression (VAR) [17], were developed by including flexible exponential smoothing techniques.”
- Predicting the Price of Bitcoin Using Machine Learning (2018, methodology): “Based on the abundance of literature on the topic, the ARIMA model is a popular approach to time series forecasting (16)(17).”
- Back propagation neural network with adaptive differential evolution algorithm for time series forecasting (2015, methodology): “…models is the autoregressive integrated moving average (ARIMA), which has been used as a forecasting technique in several fields, including traffic (Kumar & Jain, 1999), energy (Ediger & Akar, 2007), economy (Khashei, Rafiei, & Bijari, 2013), tourism (Chu, 2008), and health (Yu, Kim, & Kim, 2013).”
- Physics-guided Convolutional Neural Network (PhyCNN) for Data-driven Seismic Response Modeling (2019, methodology): “For analytical approaches, autoregressive integrated moving average (ARIMA) is one of the most popular linear models for time series analysis and forecasting [28, 29], which is originated from the autoregressive models (AR), the moving average models (MA) and the auto-regressive moving average model”
- Modelling and forecasting Turkish residential electricity demand (2011, background): “Ediger and Tatlidil (2002), Keleş (2005), Ediger and Akar (2007), Hamzacebi (2007), Akay and Atak (2007) argue that previous electricity demand forecasts for Turkey were mostly unsuccessful.”