scholarly journals Benchmarking GHG Emissions Forecasting Models for Global Climate Policy

Electronics ◽  
2021 ◽  
Vol 10 (24) ◽  
pp. 3149
Author(s):  
Cristiana Tudor ◽  
Robert Sova
Keyword(s):  
Neural Network ◽  
Developing Economies ◽  
Global Climate ◽  
Arima Model ◽  
State Space Model ◽  
Ghg Emissions ◽  
Warning System ◽  
Primary Objective ◽  
The Neural Network ◽  
Autoregression Model

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 forecast the evolution of GHG emissions in 12 top polluting economies by using data for the 1970–2018 period and employing six econometric and machine-learning models (the exponential smoothing state-space model (ETS), the Holt–Winters model (HW), the TBATS model, the ARIMA model, the structural time series model (STS), and the neural network autoregression model (NNAR)), along with a naive model. A battery of robustness checks is performed. Results confirm a priori expectations and consistently indicate that the neural network autoregression model (NNAR) presents the best out-of-sample forecasting performance for GHG emissions at different forecasting horizons by reporting the lowest average RMSE (root mean square error) and MASE (mean absolute scaled error) within the array of predictive models. Predictions made by the NNAR model for the year 2030 indicate that total GHG emissions are projected to increase by 3.67% on average among the world’s 12 most polluting countries until 2030. Only four top polluters will record decreases in total GHG emissions values in the coming decades (i.e., Canada, the Russian Federation, the US, and China), although their emission levels will remain in the upper decile. Emission increases in a handful of developing economies will see significant growth rates (a 22.75% increase in GHG total emissions in Brazil, a 15.75% increase in Indonesia, and 7.45% in India) that are expected to offset the modest decreases in GHG emissions projected for the four countries. Our findings, therefore, suggest that the world’s top polluters cannot meet assumed pollution reduction targets in the form of NDCs under the Paris agreement. Results thus highlight the necessity for more impactful policies and measures to bring the set targets within reach.

scholarly journals Research on GDP Forecast Analysis Combining BP Neural Network and ARIMA Model

2021 ◽  
Vol 2021 ◽  
pp. 1-10
Author(s):  
Shaobo Lu
Keyword(s):  
Neural Network ◽  
Time Series ◽  
Prediction Model ◽  
Error Correction ◽  
Relative Error ◽  
Bp Neural Network ◽  
Arima Model ◽  
Forecast Model ◽  
Price Prediction ◽  
The Neural Network

Based on the BP neural network and the ARIMA model, this paper predicts the nonlinear residual of GDP and adds the predicted values of the two models to obtain the final predicted value of the model. First, the focus is on the ARMA model in the univariate time series. However, in real life, forecasts are often affected by many factors, so the following introduces the ARIMAX model in the multivariate time series. In the prediction process, the network structure and various parameters of the neural network are not given in a systematic way, so the operation of the neural network is affected by many factors. Each forecasting method has its scope of application and also has its own weaknesses caused by the characteristics of its own model. Secondly, this paper proposes an effective combination method according to the GDP characteristics and builds an improved algorithm BP neural network price prediction model, the research on the combination of GDP prediction model is currently mostly focused on the weighted form, and this article proposes another combination, namely, error correction. According to the price characteristics, we determine the appropriate number of hidden layer nodes and build a BP neural network price prediction model based on the improved algorithm. Validation of examples shows that the error-corrected GDP forecast model is also better than the weighted GDP forecast model, which shows that error correction is also a better combination of forecasting methods. The forecast results of BP neural network have lower errors and monthly prices. The relative error of prediction is about 2.5%. Through comparison with the prediction results of the ARIMA model, in the daily price prediction, the relative error of the BP neural network prediction is 1.5%, which is lower than the relative error of the ARIMA model of 2%.

Decision Support System for Enhancing Crop Productivity

2020 ◽  
Vol 17 (9) ◽  
pp. 4438-4441
Author(s):  
Meeradevi ◽  
Monica R. Mundada ◽  
Hrishikesh Salpekar
Keyword(s):  
Neural Network ◽  
Neural Networks ◽  
Decision Support ◽  
Neural Network Model ◽  
Arima Model ◽  
Market Price ◽  
Crop Productivity ◽  
Market Prices ◽  
The People ◽  
The Neural Network

Agriculture is the important aspect for the people of India. The life of large percentage of people in India is dependent on agriculture. The farmers are facing difficulty in selling their product to the markets due to lack of knowledge on crop prices. The market prices changes drastically in time. Using neural networks market price can be predicted and made available to the farmers to decide the time to sell their product. The ARIMA model is used to forecast the prices which can help the farmers to improve their economy and also the crop yield is predicted using neural network in the proposed system. So, that the user can check the yield of the crop in the particular piece of land before sowing. The prediction using the neural network model results in deciding the time to sell the prices and what will be the production of the crop over the year.

scholarly journals Prediction of Area and Production of Groundnut Using Box-Jenkins Arima and Neural Network Approach

2020 ◽  
Author(s):  
S. T. Pavana Kumar ◽  
Ferdinand B. Lyngdoh
Keyword(s):  
Neural Network ◽  
Moving Average ◽  
Arima Model ◽  
Network Models ◽  
Arima Models ◽  
Neural Network Models ◽  
Neural Network Approach ◽  
Data Set ◽  
The Neural Network ◽  
Hidden Layer

Selection of parameters for Auto Regressive Integrated Moving Average (ARIMA) model in the prediction process is one of the most important tasks. In the present study, groundnut data was utlised to decide appropriate p, d, q parameters for ARIMA model for the prediction purpose. Firstly, the models were fit to data without splitting into training and validation/testing sets and evaluated for their efficiency in predicting the area and production of groundnut over the years. Meanwhile, models are compared among other fitted ARIMA models with different p, d, q parameters based on decision criteria’s viz., ME, RMSE, MAPE, AIC, BIC and R-Square. The ARIMA model with parameters p-2 d-1-2, q-1-2 are found adequate in predicting the area as well as production of groundnut. The model ARIMA (2, 2, 2) and ARIMA (2,1,1) predicted the area of groundnut crop with minimum error estimates and residual characteristics (ei). The models were fit into split data i.e., training and test data set, but these models’ prediction power (R-Square) declined during testing. In case of predicting the area, ARIMA (2,2,2) was consistent over the split data but it was not consistent while predicting the production over years. Feed-forward neural networks with single hidden layer were fit to complete, training and split data. The neural network models provided better estimates compared to Box-Jenkins ARIMA models. The data was analysed using R-Studio.

scholarly journals Fuzzy First-Order Transition-Rules-Trained Hybrid Forecasting System for Short-Term Wind Speed Forecasts

Energies ◽  
2020 ◽  
Vol 13 (13) ◽  
pp. 3332 ◽  
Author(s):  
Yulong Bai ◽  
Lihong Tang ◽  
Manhong Fan ◽  
Xiaoyan Ma ◽  
Yang Yang
Keyword(s):  
Neural Network ◽  
Hybrid System ◽  
Arima Model ◽  
Order Transition ◽  
First Order ◽  
The Neural Network ◽  
In Series ◽  
The Mean ◽  
First Order Transition ◽  
Transition Rules

Due to the ever-increasing environmental pollution becoming progressively more serious, wind power has been widely used around the world in recent years. However, because of their randomness and intermittence, the accurate prediction of wind speeds is difficult. To address this problem, this article proposes a hybrid system for short-wind-speed prediction. The system combines the autoregressive differential moving average (ARIMA) model with a three-layer feedforward neural network. An ARIMA model was employed to predict linear patterns in series, while a feedforward neural network was used to predict the nonlinear patterns in series. To improve accuracy of the predictions, the neural network models were trained by using two methods: first-order transition rules and fuzzy first-order transition rules. The Levenberg–Marquardt (LM) algorithm was applied to update the weight and deviation of each layer of neural network. The dominance matrix method was employed to calculate the weight of the hybrid system, which was used to establish the linear hybrid system. To evaluate the performance, three statistical indices were used: the mean square error (MSE), the root mean square error (RMSE) and the mean absolute percentage error (MAPE). A set of Lorenz-63 simulated values and two datasets collected from different wind fields in Qilian County, Qinghai Province, China, were utilized as to perform a comparative study. The results show the following: (a) compared with the neural network trained by first-order transition rules, the prediction accuracy of the neural network trained by the fuzzy first-order transition rules was higher; (b) the proposed hybrid system attains superior performance compared with a single model; and (c) the proposed hybrid system balances the forecast accuracy and convergence speed simultaneously during forecasting. Therefore, it was feasible to apply the hybrid model to the prediction of real time-series.

A Combined ARIMA and Neural Network Approach for Time Series Forecasting

2011 ◽  
pp. 213-225 ◽  
Author(s):  
G. Peter Zhang
Keyword(s):  
Neural Network ◽  
Time Series ◽  
Neural Network Model ◽  
Arima Model ◽  
Time Series Forecasting ◽  
Network Approach ◽  
Neural Network Approach ◽  
Individual Model ◽  
The Neural Network ◽  
Forecasting Performance

This chapter presents a combined ARIMA and neural network approach for time series forecasting. The model contains three steps: (1) fitting a linear ARIMA model to the time series under study, (2) building a neural network model based on the residuals from the ARIMA model, and (3) combine the ARIMA prediction and the neural network result to form the final forecast. By combining different models, we aim to take advantage of the unique modeling capability of each individual model and improve forecasting performance dramatically. The effectiveness of the combining approach is demonstrated and discussed with three applications.

scholarly journals Study of BP neural network based on improved particle swarm algorithm

2021 ◽  
Vol 2083 (3) ◽  
pp. 032041
Author(s):  
Xiaoqian Ma ◽  
Liyuan Li
Keyword(s):  
Neural Network ◽  
Bp Neural Network ◽  
Time Series Data ◽  
Arima Model ◽  
Particle Swarm ◽  
Correction Method ◽  
Series Data ◽  
Particle Swarm Algorithm ◽  
The Neural Network ◽  
Swarm Algorithm

Abstract This paper uses first-order difference to transform non-smooth data into smooth time series data, determines the p and q parameters in the model by judging the trailing and truncated nature of ACF, PACF, and finally establishes the ARIMA model after ACI, BCI detection. According to the parameters of the neural network randomly selected similar to the initial spatial position of the particles in the particle swarm algorithm, the improved particle swarm algorithm is used instead of the gradient correction method to precisely adjust the parameters and establish the BP neural network, which improves the robustness and accuracy of the prediction model.

Sensitivity Analysis Applied to Artificial Neural Networks for Forecasting Time Series

Methodology ◽  
2008 ◽  
Vol 4 (2) ◽  
pp. 80-86 ◽  
Author(s):  
A. Palmer ◽  
J.J. Montaño ◽  
F.J. Franconetti
Keyword(s):  
Neural Network ◽  
Time Series ◽  
Sensitivity Analysis ◽  
Network Model ◽  
Arima Model ◽  
Balearic Islands ◽  
Forecasting Accuracy ◽  
Traditional Procedure ◽  
The Neural Network ◽  
In Transit

This paper presents a novel procedure known as sensitivity analysis applied to a multilayer perceptron (MLP), which allows the most relevant lagged terms in time series forecasting to be identified. Second, this paper conducts a comparison of forecasting accuracy between the neural network model resulting from applying the sensitivity analysis to the network model derived from the traditional procedure and the classic ARIMA model – using the time series corresponding to the number of passengers in transit through the Balearic Islands. Our findings demonstrate that a neural network derived from sensitivity analysis provides the greatest forecasting accuracy.

Neural Network for Automatic Analysis of Motility Data

1994 ◽  
Vol 33 (01) ◽  
pp. 157-160 ◽  
Author(s):  
S. Kruse-Andersen ◽  
J. Kolberg ◽  
E. Jakobsen
Keyword(s):  
Neural Network ◽  
Muscular Activity ◽  
Recording System ◽  
Automatic Analysis ◽  
Biologically Relevant ◽  
System A ◽  
The Neural Network ◽  
Valuable Method ◽  
Motor Abnormalities ◽  
Trained Network

Abstract:Continuous recording of intraluminal pressures for extended periods of time is currently regarded as a valuable method for detection of esophageal motor abnormalities. A subsequent automatic analysis of the resulting motility data relies on strict mathematical criteria for recognition of pressure events. Due to great variation in events, this method often fails to detect biologically relevant pressure variations. We have tried to develop a new concept for recognition of pressure events based on a neural network. Pressures were recorded for over 23 hours in 29 normal volunteers by means of a portable data recording system. A number of pressure events and non-events were selected from 9 recordings and used for training the network. The performance of the trained network was then verified on recordings from the remaining 20 volunteers. The accuracy and sensitivity of the two systems were comparable. However, the neural network recognized pressure peaks clearly generated by muscular activity that had escaped detection by the conventional program. In conclusion, we believe that neu-rocomputing has potential advantages for automatic analysis of gastrointestinal motility data.

Decision Support for Psychiatric Diagnosis Based on a Simple Questionnaire

1997 ◽  
Vol 36 (04/05) ◽  
pp. 349-351
Author(s):  
H. Mizuta ◽  
K. Kawachi ◽  
H. Yoshida ◽  
K. Iida ◽  
Y. Okubo ◽  
...  
Keyword(s):  
Neural Network ◽  
Decision Support ◽  
Psychiatric Diagnosis ◽  
Psychiatric Patients ◽  
Bayesian Classifier ◽  
Neural Network Classifier ◽  
Correct Decision ◽  
Neurotic Disorders ◽  
The Neural Network

Abstract:This paper compares two classifiers: Pseudo Bayesian and Neural Network for assisting in making diagnoses of psychiatric patients based on a simple yes/no questionnaire which is provided at the outpatient’s first visit to the hospital. The classifiers categorize patients into three most commonly seen ICD classes, i.e. schizophrenic, emotional and neurotic disorders. One hundred completed questionnaires were utilized for constructing and evaluating the classifiers. Average correct decision rates were 73.3% for the Pseudo Bayesian Classifier and 77.3% for the Neural Network classifier. These rates were higher than the rate which an experienced psychiatrist achieved based on the same restricted data as the classifiers utilized. These classifiers may be effectively utilized for assisting psychiatrists in making their final diagnoses.

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