An Advanced Deep Learning Model for Short-Term Forecasting U.S. Natural Gas Price and Movement

Livieris, Ioannis E.; Pintelas, Emmanuel G.; Kiriakidou, Niki; Stavroyiannis, Stavros

doi:10.1007/978-3-030-49190-1_15

Cited by 22 publications

(11 citation statements)

References 15 publications

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“…However, they do not evaluate whether the model properly fits the data while the residuals are usually dedicated to evaluating this. Therefore, we evaluated the forecasting reliability of the proposed models by examining for auto-correlation in the errors [35,36]. Figures 3-5 illustrate the autocorrelation function (ACF) diagram for Dez reservoir inflow forecasting by FSF-ARIMA model, RANN model, and Hybrid model, respectively.…”

Section: Resultsmentioning

confidence: 99%

“…Although the hybrid model outperformed the RANN and FSF-ARIMA models, the ACF plots revealed that all models were unable to make reliable forecasts. It is worth mentioning that employing a more advanced model such as long short-term memory (LSTM) and deep learning techniques like the Convolutional Neural Network (CNN), as well as their combination, CNN-LSTM [35,36], can improve the accuracy of forecasting. In addition, these new models have shown more reliable forecasts [35,36].…”

mentioning

confidence: 99%

“…It is worth mentioning that employing a more advanced model such as long short-term memory (LSTM) and deep learning techniques like the Convolutional Neural Network (CNN), as well as their combination, CNN-LSTM [35,36], can improve the accuracy of forecasting. In addition, these new models have shown more reliable forecasts [35,36]. Therefore, there are open avenues to compare the results of this study with LSTM and CNN-LSTM in future investigations.…”

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confidence: 99%

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Improving Daily Peak Flow Forecasts Using Hybrid Fourier-Series Autoregressive Integrated Moving Average and Recurrent Artificial Neural Network Models

Banihabib

Bandari

Valipour

2020

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In multi-purpose reservoirs, to achieve optimal operation, sophisticated models are required to forecast reservoir inflow in both short- and long-horizon times with an acceptable accuracy, particularly for peak flows. In this study, an auto-regressive hybrid model is proposed for long-horizon forecasting of daily reservoir inflow. The model is examined for a one-year horizon forecasting of high-oscillated daily flow time series. First, a Fourier-Series Filtered Autoregressive Integrated Moving Average (FSF-ARIMA) model is applied to forecast linear behavior of daily flow time series. Second, a Recurrent Artificial Neural Network (RANN) model is utilized to forecast FSF-ARIMA model’s residuals. The hybrid model follows the detail of observed flow time variation and forecasted peak flow more accurately than previous models. The proposed model enhances the ability to forecast reservoir inflow, especially in peak flows, compared to previous linear and nonlinear auto-regressive models. The hybrid model has a potential to decrease maximum and average forecasting error by 81% and 80%, respectively. The results of this investigation are useful for stakeholders and water resources managers to schedule optimum operation of multi-purpose reservoirs in controlling floods and generating hydropower.

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Section: Resultsmentioning

confidence: 99%

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confidence: 99%

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confidence: 99%

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Improving Daily Peak Flow Forecasts Using Hybrid Fourier-Series Autoregressive Integrated Moving Average and Recurrent Artificial Neural Network Models

Banihabib

Bandari

Valipour

2020

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“…As the LSTM networks are designed to deal with temporal correlations, they utilize only the features provided with the training set [28]. The convolutional layers of CNNs can extract more valuable features by filtering out the noise prevailing in the raw input data [29]. They are also capable of scooping the hidden features that otherwise could not be pulled out by using LSTM.…”

Section: Solution Approaches and The Proposed Solutionmentioning

confidence: 99%

“…Considering the potential, deep learning has been applied in various domains and applications for different purposes [ Many researchers exploited the convolutional aspect of CNN in combination with LSTM to improve the performance of time-series prediction/forecasting in various applications, such as for inventory prediction [30], stock price prediction [63] [64] [65] [66], gold price forecasting [28], Bitcoin price forecasting [67], tourist flow forecasting [68], sentiment prediction of social media users [69], household power consumption prediction [70] [27], photovoltaic power prediction [71], wind power forecasting [72], PM2.5 prediction [73] [74], predicting NOx emission in processing of heavy oil [75], forecasting natural gas price and movement [29], urban expansion prediction [76], predicting waterworks operations at a water purification plant [77], predicting sea surface temperature [78], typhoon formation forecasting [79], crop yield prediction [80], COVID-19 detection and predictions [81] [82] [83], human age estimation [84], and so on. [106] used LSTM to predict the availability of mobile edge computing-enabled base stations depending on the vehicle's mobility for offloading the computation jobs from the vehicle to the base station.…”

Section: B Deep Learning For Resource Management and Predictionmentioning

confidence: 99%

Deep Learning Based Resource Availability Prediction for Local Mobile Crowd Computing

et al. 2021

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Mobile crowd computing (MCC) that utilizes public-owned (crowd's) smart mobile devices (SMDs) collectively can give adequate computing power without any additional financial and ecological cost. However, the major challenge is to cope with the mobility (or availability) issue of SMDs. User's unpredicted mobility makes the SMDs really unstable resources. Selecting such erratic resources for job schedule would result in frequent job offloading and, in the worst case, job loss, which would affect the overall performance and the quality of service of MCC. In a Local MCC, generally, a set of users are available for a certain period regularly. Based on this information, the chances of a user being available for a certain duration from a given point of time can be predicted. In this paper, we provide an effective model to predict the availability of the users (i.e., their SMDs) in such an MCC environment. We argue that before submitting a job to an SMD, the stability of it is to be assessed for the duration of execution of the job to be assigned. If the predicted availability time is greater than the job size, then only the job should be assigned to the SMD. An accurate prediction will minimize the unnecessary job offloading or job loss due to the early departure of the designated SMD. We propose an advanced convolutional feature extraction mechanism that is applied to LSTM and GRU-based time-series prediction models for predicting SMD availability. To collect user mobility data, we considered a research lab scenario, where real mobility traces were recorded with respect to a Wi-Fi access point. We compared the prediction performances of convolutional LSTM and GRU with the basic LSTM and GRU and ARIMA in terms of MAE, RMSE, R 2 , accuracy, and perplexity. In all the measurements, the proposed convolutional LSTM exhibits considerably better prediction performance.

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Distributional modeling and forecasting of natural gas prices

Berrisch

Ziel

2022

Journal of Forecasting

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We examine the problem of modeling and forecasting European day‐ahead and month‐ahead natural gas prices. For this, we propose two distinct probabilistic models that can be utilized in risk and portfolio management. We use daily pricing data ranging from 2011 to 2020. Extensive descriptive data analysis shows that both time series feature heavy tails and conditional heteroscedasticity and show asymmetric behavior in their differences. We propose state‐space time series models under skewed, heavy‐tailed distributions to capture all stylized facts of the data. They include the impact of autocorrelation, seasonality, risk premia, temperature, storage levels, the price of European Emission Allowances, and related fuel prices of oil, coal, and electricity. We provide rigorous model diagnostics and interpret all model components in detail. Additionally, we conduct a probabilistic forecasting study with significance tests and compare the predictive performance against literature benchmarks. The proposed day‐ahead (month‐ahead) model leads to a 13% (9%) reduction in out‐of‐sample continuous ranked probability score (CRPS) compared with the best performing benchmark model, mainly due to adequate modeling of the volatility and heavy tails.

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An Advanced Deep Learning Model for Short-Term Forecasting U.S. Natural Gas Price and Movement

Cited by 22 publications

References 15 publications

Improving Daily Peak Flow Forecasts Using Hybrid Fourier-Series Autoregressive Integrated Moving Average and Recurrent Artificial Neural Network Models

Improving Daily Peak Flow Forecasts Using Hybrid Fourier-Series Autoregressive Integrated Moving Average and Recurrent Artificial Neural Network Models

Deep Learning Based Resource Availability Prediction for Local Mobile Crowd Computing

Distributional modeling and forecasting of natural gas prices

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