In power market, electricity price forecasting provides significant information which can help the electricity market participants to prepare corresponding bidding strategies to maximize their profits. This paper intr...In power market, electricity price forecasting provides significant information which can help the electricity market participants to prepare corresponding bidding strategies to maximize their profits. This paper introduces the models of autoregressive integrated moving average (ARIMA) and artificial neural network (ANN) which are applied to the price forecasts for up to 3 steps 8 weeks ahead in the UK electricity market. The half hourly data of historical prices are obtained from UK Reference Price Data from March 22nd to July 14th 2010 and the predictions are derived from a sliding training window with a length of 8 weeks. The ARIMA with various AR and MA orders and the ANN with different numbers of delays and neurons have been established and compared in terms of the root mean square errors (RMSEs) of price forecasts. The experimental results illustrate that the ARIMA (4,1,2) model gives greater improvement over persistence than the ANN (20 neurons, 4 delays) model.展开更多
Vulnerability forecasting models help us to predict the number of vulnerabilities that may occur in the future for a given Operating System (OS). There exist few models that focus on quantifying future vulnerabilities...Vulnerability forecasting models help us to predict the number of vulnerabilities that may occur in the future for a given Operating System (OS). There exist few models that focus on quantifying future vulnerabilities without consideration of trend, level, seasonality and non linear components of vulnerabilities. Unlike traditional ones, we propose a vulnerability analytic prediction model based on linear and non-linear approaches via time series analysis. We have developed the models based on Auto Regressive Moving Average (ARIMA), Artificial Neural Network (ANN), and Support Vector Machine (SVM) settings. The best model which provides the minimum error rate is selected for prediction of future vulnerabilities. Utilizing time series approach, this study has developed a predictive analytic model for three popular Desktop Operating Systems, namely, Windows 7, Mac OS X, and Linux Kernel by using their reported vulnerabilities on the National Vulnerability Database (NVD). Based on these reported vulnerabilities, we predict ahead their behavior so that the OS companies can make strategic and operational decisions like secure deployment of OS, facilitate backup provisioning, disaster recovery, diversity planning, maintenance scheduling, etc. Similarly, it also helps in assessing current security risks along with estimation of resources needed for handling potential security breaches and to foresee the future releases of security patches. The proposed non-linear analytic models produce very good prediction results in comparison to linear time series models.展开更多
Time series forecasting research area mainly focuses on developing effective forecasting models toimprove prediction accuracy. An ensemble model composed of autoregressive integrated movingaverage （ARIMA）, artificia...Time series forecasting research area mainly focuses on developing effective forecasting models toimprove prediction accuracy. An ensemble model composed of autoregressive integrated movingaverage （ARIMA）, artificial neural network （ANN）, restricted Boltzmann machines （RBM）, anddiscrete wavelet transform （DWT） is presented in this paper. In the proposed model, DWT firstdecomposes time series into approximation and detail. Then Khashei and Bijari＇s model, which is anensemble model of ARIMA and ANN, is applied to the approximation and detail to extract their bothlinear and nonlinear components and fit the relationship between the components as a function insteadof additive relationship. Furthermore, RBM is used to perform pre-training for generating initialweights and biases based on inputs feature for ANN. Finally, the forecasted approximation and detailare combined to obtain final forecasting. The forecasting capability of the proposed model is testedwith three well-known time series： sunspot, Canadian lynx, exchange rate time series. The predictionperformance is compared to the other six forecasting models. The results indicate that the proposedmodel gives the best performance in all three data sets and all three measures （i.e. MSE, MAE andMAPE）.展开更多
In deregulated electricity markets, price forecasting is gaining importance between various market players in the power in order to adjust their bids in the day-ahead electricity markets and maximize their profits. El...In deregulated electricity markets, price forecasting is gaining importance between various market players in the power in order to adjust their bids in the day-ahead electricity markets and maximize their profits. Electricity price is volatile but non random in nature making it possible to identify the patterns based on the historical data and forecast. An accurate price forecasting method is an important factor for the market players as it enables them to decide their bidding strategy to maximize profits. Various models have been developed over a period of time which can be broadly classified into two types of models that are mainly used for Electricity Price forecasting are: 1) Time series models;and 2) Simulation based models;time series models are widely used among the two, for day ahead forecasting. The presented work summarizes the influencing factors that affect the price behavior and various established forecasting models based on time series analysis, such as Linear regression based models, nonlinear heuristics based models and other simulation based models.展开更多
Today, COVID-19 pandemic has become the greatest worldwide threat, as it spreads rapidly among individuals in most countries around the world. This study concerns the problem of daily prediction of new COVID-19 cases ...Today, COVID-19 pandemic has become the greatest worldwide threat, as it spreads rapidly among individuals in most countries around the world. This study concerns the problem of daily prediction of new COVID-19 cases in Italy, aiming to find the best predictive model for daily infection number in countries with a large number of confirmed cases. Finding the most accurate forecasting model would help allocate medical resources, handle the spread of the pandemic and get more prepared in terms of health care systems. We compare the forecasting performance of linear and nonlinear forecasting models using daily COVID-19 data for the period between 22 February 2020 and 10 January 2022. We discuss various forecasting approaches, including an Autoregressive Integrated Moving Average (ARIMA) model, a Nonlinear Autoregressive Neural Network (NARNN) model, a TBATS model and Exponential Smoothing on the data collected from 22 February 2020 to 10 January 2022 and compared their accuracy using the data collected from 26 March 2020 to 04 April 2020, choosing the model with the lowest Mean Absolute Percentage Error (MAPE) value. Since the linear models seem not to easily follow the nonlinear patterns of daily confirmed COVID-19 cases, Artificial Neural Network (ANN) has been successfully applied to solve problems of forecasting nonlinear models. The model has been used for daily prediction of COVID-19 cases for the next 20 days without any additional intervention. The prediction model can be applied to other countries struggling with the COVID-19 pandemic and to any possible future pandemics.展开更多
文摘In power market, electricity price forecasting provides significant information which can help the electricity market participants to prepare corresponding bidding strategies to maximize their profits. This paper introduces the models of autoregressive integrated moving average (ARIMA) and artificial neural network (ANN) which are applied to the price forecasts for up to 3 steps 8 weeks ahead in the UK electricity market. The half hourly data of historical prices are obtained from UK Reference Price Data from March 22nd to July 14th 2010 and the predictions are derived from a sliding training window with a length of 8 weeks. The ARIMA with various AR and MA orders and the ANN with different numbers of delays and neurons have been established and compared in terms of the root mean square errors (RMSEs) of price forecasts. The experimental results illustrate that the ARIMA (4,1,2) model gives greater improvement over persistence than the ANN (20 neurons, 4 delays) model.
文摘Vulnerability forecasting models help us to predict the number of vulnerabilities that may occur in the future for a given Operating System (OS). There exist few models that focus on quantifying future vulnerabilities without consideration of trend, level, seasonality and non linear components of vulnerabilities. Unlike traditional ones, we propose a vulnerability analytic prediction model based on linear and non-linear approaches via time series analysis. We have developed the models based on Auto Regressive Moving Average (ARIMA), Artificial Neural Network (ANN), and Support Vector Machine (SVM) settings. The best model which provides the minimum error rate is selected for prediction of future vulnerabilities. Utilizing time series approach, this study has developed a predictive analytic model for three popular Desktop Operating Systems, namely, Windows 7, Mac OS X, and Linux Kernel by using their reported vulnerabilities on the National Vulnerability Database (NVD). Based on these reported vulnerabilities, we predict ahead their behavior so that the OS companies can make strategic and operational decisions like secure deployment of OS, facilitate backup provisioning, disaster recovery, diversity planning, maintenance scheduling, etc. Similarly, it also helps in assessing current security risks along with estimation of resources needed for handling potential security breaches and to foresee the future releases of security patches. The proposed non-linear analytic models produce very good prediction results in comparison to linear time series models.
文摘Time series forecasting research area mainly focuses on developing effective forecasting models toimprove prediction accuracy. An ensemble model composed of autoregressive integrated movingaverage （ARIMA）, artificial neural network （ANN）, restricted Boltzmann machines （RBM）, anddiscrete wavelet transform （DWT） is presented in this paper. In the proposed model, DWT firstdecomposes time series into approximation and detail. Then Khashei and Bijari＇s model, which is anensemble model of ARIMA and ANN, is applied to the approximation and detail to extract their bothlinear and nonlinear components and fit the relationship between the components as a function insteadof additive relationship. Furthermore, RBM is used to perform pre-training for generating initialweights and biases based on inputs feature for ANN. Finally, the forecasted approximation and detailare combined to obtain final forecasting. The forecasting capability of the proposed model is testedwith three well-known time series： sunspot, Canadian lynx, exchange rate time series. The predictionperformance is compared to the other six forecasting models. The results indicate that the proposedmodel gives the best performance in all three data sets and all three measures （i.e. MSE, MAE andMAPE）.
文摘In deregulated electricity markets, price forecasting is gaining importance between various market players in the power in order to adjust their bids in the day-ahead electricity markets and maximize their profits. Electricity price is volatile but non random in nature making it possible to identify the patterns based on the historical data and forecast. An accurate price forecasting method is an important factor for the market players as it enables them to decide their bidding strategy to maximize profits. Various models have been developed over a period of time which can be broadly classified into two types of models that are mainly used for Electricity Price forecasting are: 1) Time series models;and 2) Simulation based models;time series models are widely used among the two, for day ahead forecasting. The presented work summarizes the influencing factors that affect the price behavior and various established forecasting models based on time series analysis, such as Linear regression based models, nonlinear heuristics based models and other simulation based models.
文摘Today, COVID-19 pandemic has become the greatest worldwide threat, as it spreads rapidly among individuals in most countries around the world. This study concerns the problem of daily prediction of new COVID-19 cases in Italy, aiming to find the best predictive model for daily infection number in countries with a large number of confirmed cases. Finding the most accurate forecasting model would help allocate medical resources, handle the spread of the pandemic and get more prepared in terms of health care systems. We compare the forecasting performance of linear and nonlinear forecasting models using daily COVID-19 data for the period between 22 February 2020 and 10 January 2022. We discuss various forecasting approaches, including an Autoregressive Integrated Moving Average (ARIMA) model, a Nonlinear Autoregressive Neural Network (NARNN) model, a TBATS model and Exponential Smoothing on the data collected from 22 February 2020 to 10 January 2022 and compared their accuracy using the data collected from 26 March 2020 to 04 April 2020, choosing the model with the lowest Mean Absolute Percentage Error (MAPE) value. Since the linear models seem not to easily follow the nonlinear patterns of daily confirmed COVID-19 cases, Artificial Neural Network (ANN) has been successfully applied to solve problems of forecasting nonlinear models. The model has been used for daily prediction of COVID-19 cases for the next 20 days without any additional intervention. The prediction model can be applied to other countries struggling with the COVID-19 pandemic and to any possible future pandemics.
