Forecasting ethanol demand in India to meet future blending targets: A comparison of ARIMA and various regression models

Dey, Bishal; Roy, Bidesh; Datta, Subir; Ustun, Taha Selim

doi:10.1016/j.egyr.2022.11.038

Cited by 24 publications

(9 citation statements)

References 31 publications

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“…During the initial stages, a considerable number of studies utilized probability statistical approaches or probability distribution methods to predict cellular traffic, including models like auto-regression moving average (ARMA) and auto-regressive integrated moving average (ARIMA) [15][16][17]. For instance, Yang, H., et al [18] proposed a cellular network traffic prediction model that integrates simulated annealing (SA), ARIMA, and a backpropagation neural network (BPNN).…”

Section: Related Workmentioning

confidence: 99%

DBSTGNN-Att: Dual Branch Spatio-Temporal Graph Neural Network with an Attention Mechanism for Cellular Network Traffic Prediction

Cai,

Tan,

Zhang

et al. 2024

Applied Sciences

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As network technology continues to develop, the popularity of various intelligent terminals has accelerated, leading to a rapid growth in the scale of wireless network traffic. This growth has resulted in significant pressure on resource consumption and network security maintenance. The objective of this paper is to enhance the prediction accuracy of cellular network traffic in order to provide reliable support for the subsequent base station sleep control or the identification of malicious traffic. To achieve this target, a cellular network traffic prediction method based on multi-modal data feature fusion is proposed. Firstly, an attributed K-nearest node (KNN) graph is constructed based on the similarity of data features, and the fused high-dimensional features are incorporated into the graph to provide more information for the model. Subsequently, a dual branch spatio-temporal graph neural network with an attention mechanism (DBSTGNN-Att) is designed for cellular network traffic prediction. Extensive experiments conducted on real-world datasets demonstrate that the proposed method outperforms baseline models, such as temporal graph convolutional networks (T-GCNs) and spatial–temporal self-attention graph convolutional networks (STA-GCNs) with lower mean absolute error (MAE) values of 6.94% and 2.11%, respectively. Additionally, the ablation experimental results show that the MAE of multi-modal feature fusion using the attributed KNN graph is 8.54% lower compared to that of the traditional undirected graphs.

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Section: Related Workmentioning

confidence: 99%

DBSTGNN-Att: Dual Branch Spatio-Temporal Graph Neural Network with an Attention Mechanism for Cellular Network Traffic Prediction

Cai,

Tan,

Zhang

et al. 2024

Applied Sciences

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“…After identification, an ARIMA model is estimated for a specific smooth time series. The simple ARIMA model is estimated based on the number of effective coefficients, the Bayesian information criterion (BIC) and the Akaike information criterion (AIC), and the adjusted R 2 [35]. After estimation, the selected ARIMA model needs to be diagnosed to check if the residuals are white noise.…”

Section: Time Series Forecasting Modelmentioning

confidence: 99%

Analysis of the Composition of Ancient Glass and Its Identification Based on the Daen-LR, ARIMA-LSTM and MLR Combined Process

Wang

et al. 2023

Applied Sciences

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The glass relics are precious material evidence of the early trade and cultural exchange between the East and the West. To explore the cultural differences and trade development between early China and foreign countries, it is extremely important to classify glass cultural relics. Despite their similar appearances, Chinese glass contains more lead, while foreign glass contains more potassium. In view of this, this paper proposes a joint Daen-LR, ARIMA-LSTM, and MLR machine learning algorithm (JMLA) for the analysis and identification of the chemical composition of ancient glass. We separate the sampling points of ancient glass into two systems: lead-barium glass and high-potassium glass. Firstly, an improved logistic regression model based on a double adaptive elastic network (Daen-LR) is used to select variables with both Oracle and adaptive classification characteristics. Secondly, the ARIMA-LSTM model was used to establish the correlation curve of chemical composition before and after weathering and to predict the change in chemical composition with weathering. Thirdly, combining the data processed by the above two methods, a multiple linear regression model (MLR) is used to classify unknown glass products. It was shown that the sample obtained by this processing method has a very good fit. In comparison with other similar types of models like Decision Trees (DT), Random Forests (RF), Support Vector Machines (SVM), and Random Forests based on classification and regression trees (CART-RF), the classification accuracy of JMLA is 97.9% on the train set. The accuracy rate on the test set reached 97.6%. The results of the research demonstrate that JMLA can improve the accuracy of the glass type classification problem, greatly enhance the research efficiency of archaeological staff, and gain a more reliable result.

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“…The ARIMA model is a flexible and widely used method for forecasting time series data [34,35]. An ARIMA model is commonly denoted as (p, d, q), where p is the number of the autoregressive terms, q denotes the number of moving average terms, and d indicates the number of differences required for stationarity [36]. In this study, six different tentative ARIMA models: ARIMA (2, 1, 1), ARIMA (2, 0, 1), ARIMA (0, 2, 3), ARIMA (1, 1, 0), ARIMA (1, 0, 0), and ARIMA (0, 2, 3), were fitted to the data.…”

Section: Autoregressive Integrated Moving Average (Arima) Modelmentioning

confidence: 99%

Assessing the Capacity of the Water–Energy–Food Nexus in Enhancing Sustainable Agriculture and Food Security in Burundi

Mperejekumana,

Shen,

Zhong

et al. 2023

Sustainability

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In Burundi, a significant portion of the population heavily relies on agriculture for both sustenance and income. However, persistently low agricultural yields place approximately 1.8 million people at immediate risk of food insecurity. The purpose of this study was to explore the potential of the water–energy–food (WEF) nexus approach to strengthening agricultural sustainability and improving food security in Burundi. This study employs both the ARDL model and the ARIMA model to analyze the impact of water, energy, and land on agricultural yield while also projecting their future dynamics in Burundi. The results highlight a positive correlation between these resources and agricultural yield, demonstrating that a 1% increase in each of these variables would collectively result in a 3.74% increase in agricultural yield. Furthermore, the predictive findings reveal an anticipated decrease in agricultural yield by approximately 74.9 kg ha−1 and a reduction in agricultural land spanning up to 11.9 × 104 hectares by the year 2030. As a contribution to the body of knowledge, this study introduces a framework for the WEF nexus and sustainable agriculture, providing fresh perspectives to the literature on resource nexus studies in Burundi and among other practitioners in Africa.

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Forecasting ethanol demand in India to meet future blending targets: A comparison of ARIMA and various regression models

Cited by 24 publications

References 31 publications

DBSTGNN-Att: Dual Branch Spatio-Temporal Graph Neural Network with an Attention Mechanism for Cellular Network Traffic Prediction

DBSTGNN-Att: Dual Branch Spatio-Temporal Graph Neural Network with an Attention Mechanism for Cellular Network Traffic Prediction

Analysis of the Composition of Ancient Glass and Its Identification Based on the Daen-LR, ARIMA-LSTM and MLR Combined Process

Assessing the Capacity of the Water–Energy–Food Nexus in Enhancing Sustainable Agriculture and Food Security in Burundi

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