scholarly journals Time-series forecasting with deep learning: a survey

2021 ◽  
Vol 379 (2194) ◽  
pp. 20200209
Author(s):  
Bryan Lim ◽  
Stefan Zohren
Keyword(s):  
Time Series ◽  
Deep Learning ◽  
Time Series Data ◽  
Time Series Forecasting ◽  
Series Data ◽  
Climate Modelling ◽  
Weather And Climate ◽  
Recent Developments ◽  
One Step ◽  
Learning Architectures

Numerous deep learning architectures have been developed to accommodate the diversity of time-series datasets across different domains. In this article, we survey common encoder and decoder designs used in both one-step-ahead and multi-horizon time-series forecasting—describing how temporal information is incorporated into predictions by each model. Next, we highlight recent developments in hybrid deep learning models, which combine well-studied statistical models with neural network components to improve pure methods in either category. Lastly, we outline some ways in which deep learning can also facilitate decision support with time-series data. This article is part of the theme issue ‘Machine learning for weather and climate modelling’.

scholarly journals Temporal Convolutional Networks Applied to Energy-Related Time Series Forecasting

2020 ◽  
Vol 10 (7) ◽  
pp. 2322 ◽  
Author(s):  
Pedro Lara-Benítez ◽  
Manuel Carranza-García ◽  
José M. Luna-Romera ◽  
José C. Riquelme
Keyword(s):  
Time Series ◽  
Deep Learning ◽  
Energy Demand ◽  
Time Series Data ◽  
Demand Forecasting ◽  
Time Series Forecasting ◽  
Series Data ◽  
Recurrent Networks ◽  
Convolutional Networks ◽  
Related Time Series

Modern energy systems collect high volumes of data that can provide valuable information about energy consumption. Electric companies can now use historical data to make informed decisions on energy production by forecasting the expected demand. Many deep learning models have been proposed to deal with these types of time series forecasting problems. Deep neural networks, such as recurrent or convolutional, can automatically capture complex patterns in time series data and provide accurate predictions. In particular, Temporal Convolutional Networks (TCN) are a specialised architecture that has advantages over recurrent networks for forecasting tasks. TCNs are able to extract long-term patterns using dilated causal convolutions and residual blocks, and can also be more efficient in terms of computation time. In this work, we propose a TCN-based deep learning model to improve the predictive performance in energy demand forecasting. Two energy-related time series with data from Spain have been studied: the national electric demand and the power demand at charging stations for electric vehicles. An extensive experimental study has been conducted, involving more than 1900 models with different architectures and parametrisations. The TCN proposal outperforms the forecasting accuracy of Long Short-Term Memory (LSTM) recurrent networks, which are considered the state-of-the-art in the field.

scholarly journals “Dust in the Wind...”, Deep Learning Application to Wind Energy Time Series Forecasting

Energies ◽  
2019 ◽  
Vol 12 (12) ◽  
pp. 2385 ◽  
Author(s):  
Jaume Manero ◽  
Javier Béjar ◽  
Ulises Cortés
Keyword(s):  
Time Series ◽  
Deep Learning ◽  
North America ◽  
Wind Energy ◽  
Prediction Models ◽  
Time Series Data ◽  
Electricity Production ◽  
Coefficient Of Determination ◽  
Series Data ◽  
Learning Architectures

To balance electricity production and demand, it is required to use different prediction techniques extensively. Renewable energy, due to its intermittency, increases the complexity and uncertainty of forecasting, and the resulting accuracy impacts all the different players acting around the electricity systems around the world like generators, distributors, retailers, or consumers. Wind forecasting can be done under two major approaches, using meteorological numerical prediction models or based on pure time series input. Deep learning is appearing as a new method that can be used for wind energy prediction. This work develops several deep learning architectures and shows their performance when applied to wind time series. The models have been tested with the most extensive wind dataset available, the National Renewable Laboratory Wind Toolkit, a dataset with 126,692 wind points in North America. The architectures designed are based on different approaches, Multi-Layer Perceptron Networks (MLP), Convolutional Networks (CNN), and Recurrent Networks (RNN). These deep learning architectures have been tested to obtain predictions in a 12-h ahead horizon, and the accuracy is measured with the coefficient of determination, the R² method. The application of the models to wind sites evenly distributed in the North America geography allows us to infer several conclusions on the relationships between methods, terrain, and forecasting complexity. The results show differences between the models and confirm the superior capabilities on the use of deep learning techniques for wind speed forecasting from wind time series data.

scholarly journals Temporal Convolutional Networks Applied to Energy-related Time Series Forecasting

2020 ◽  
Author(s):  
Pedro Lara-Benítez ◽  
Manuel Carranza-García ◽  
José M. Luna-Romera ◽  
José C. Riquelme
Keyword(s):  
Time Series ◽  
Deep Learning ◽  
Energy Demand ◽  
Time Series Data ◽  
Demand Forecasting ◽  
Time Series Forecasting ◽  
Series Data ◽  
Recurrent Networks ◽  
Convolutional Networks ◽  
Related Time Series

Modern energy systems collect high volumes of data that can provide valuable information about energy consumption. Electric companies can now use historical data to make informed decisions on energy production by forecasting the expected demand. Many deep learning models have been proposed to deal with these type of time series forecasting problems. Deep neural networks, such as recurrent or convolutional, can automatically capture complex patterns in time series data and provide accurate predictions. In particular, Temporal Convolutional Networks (TCN) are a specialised architecture that has advantages over recurrent networks for forecasting tasks. TCNs are able to extract long-term patterns using dilated causal convolutions and residual blocks, and can also be more efficient in terms of computation time. In this work, we propose a TCN-based deep learning model to improve the predictive performance in energy demand forecasting. Two energy-related time series with data from Spain have been studied: the national electric demand, and the power demand at charging stations for electric vehicles. An extensive experimental study has been conducted, involving more than 1900 models with different architectures and parametrisations. The TCN proposal outperforms the forecasting accuracy of Long Short-Term Memory (LSTM) recurrent networks, which are considered the state-of-the-art in the field.

scholarly journals Load forecasting of refrigerated display cabinet based on CEEMD–IPSO–LSTM combined model

Open Physics ◽  
2021 ◽  
Vol 19 (1) ◽  
pp. 360-374
Author(s):  
Yuan Pei ◽  
Lei Zhenglin ◽  
Zeng Qinghui ◽  
Wu Yixiao ◽  
Lu Yanli ◽  
...  
Keyword(s):  
Time Series ◽  
Deep Learning ◽  
Time Series Data ◽  
Load Forecasting ◽  
Series Data ◽  
Forecasting Model ◽  
Combined Model ◽  
Forecasting Accuracy ◽  
Forecasting Method ◽  
Consumption Reduction

Abstract The load of the showcase is a nonlinear and unstable time series data, and the traditional forecasting method is not applicable. Deep learning algorithms are introduced to predict the load of the showcase. Based on the CEEMD–IPSO–LSTM combination algorithm, this paper builds a refrigerated display cabinet load forecasting model. Compared with the forecast results of other models, it finally proves that the CEEMD–IPSO–LSTM model has the highest load forecasting accuracy, and the model’s determination coefficient is 0.9105, which is obviously excellent. Compared with other models, the model constructed in this paper can predict the load of showcases, which can provide a reference for energy saving and consumption reduction of display cabinet.

scholarly journals Deep Learning for Anomaly Detection in Time-Series Data: Review, Analysis, and Guidelines

IEEE Access ◽  
2021 ◽  
Vol 9 ◽  
pp. 120043-120065
Author(s):  
Kukjin Choi ◽  
Jihun Yi ◽  
Changhwa Park ◽  
Sungroh Yoon
Keyword(s):  
Time Series ◽  
Deep Learning ◽  
Anomaly Detection ◽  
Time Series Data ◽  
Series Data ◽  
Review Analysis

scholarly journals Implementation of IoT Framework with Data Analysis Using Deep Learning Methods for Occupancy Prediction in a Building

2021 ◽  
Vol 13 (3) ◽  
pp. 67
Author(s):  
Eric Hitimana ◽  
Gaurav Bajpai ◽  
Richard Musabe ◽  
Louis Sibomana ◽  
Jayavel Kayalvizhi
Keyword(s):  
Machine Learning ◽  
Time Series ◽  
Deep Learning ◽  
Time Series Data ◽  
Multivariate Time Series ◽  
Machine Learning Algorithms ◽  
Series Data ◽  
Support Vector ◽  
Human Beings ◽  
Feed Forward Network

Many countries worldwide face challenges in controlling building incidence prevention measures for fire disasters. The most critical issues are the localization, identification, detection of the room occupant. Internet of Things (IoT) along with machine learning proved the increase of the smartness of the building by providing real-time data acquisition using sensors and actuators for prediction mechanisms. This paper proposes the implementation of an IoT framework to capture indoor environmental parameters for occupancy multivariate time-series data. The application of the Long Short Term Memory (LSTM) Deep Learning algorithm is used to infer the knowledge of the presence of human beings. An experiment is conducted in an office room using multivariate time-series as predictors in the regression forecasting problem. The results obtained demonstrate that with the developed system it is possible to obtain, process, and store environmental information. The information collected was applied to the LSTM algorithm and compared with other machine learning algorithms. The compared algorithms are Support Vector Machine, Naïve Bayes Network, and Multilayer Perceptron Feed-Forward Network. The outcomes based on the parametric calibrations demonstrate that LSTM performs better in the context of the proposed application.

Outlier Detection Using Convolutional Neural Network for Wireless Sensor Network

2021 ◽  
Vol 17 (2) ◽  
pp. 0-0
Keyword(s):  
Neural Network ◽  
Time Series ◽  
Deep Learning ◽  
Wireless Sensor Network ◽  
Convolutional Neural Network ◽  
Sensor Network ◽  
Time Series Data ◽  
Wireless Sensor ◽  
Series Data ◽  
Detection Accuracy

Over the recent years, the term deep learning has been considered as one of the primary choice for handling huge amount of data. Having deeper hidden layers, it surpasses classical methods for detection of outlier in wireless sensor network. The Convolutional Neural Network (CNN) is a biologically inspired computational model which is one of the most popular deep learning approaches. It comprises neurons that self-optimize through learning. EEG generally known as Electroencephalography is a tool used for investigation of brain function and EEG signal gives time-series data as output. In this paper, we propose a state-of-the-art technique designed by processing the time-series data generated by the sensor nodes stored in a large dataset into discrete one-second frames and these frames are projected onto a 2D map images. A convolutional neural network (CNN) is then trained to classify these frames. The result improves detection accuracy and encouraging.

Corporation financial distress prediction with deep learning: analysis of public listed companies in Malaysia

2021 ◽  
Vol ahead-of-print (ahead-of-print) ◽  
Author(s):  
Zulkifli Halim ◽  
Shuhaida Mohamed Shuhidan ◽  
Zuraidah Mohd Sanusi
Keyword(s):  
Time Series ◽  
Deep Learning ◽  
Financial Distress ◽  
Time Series Data ◽  
Series Data ◽  
Learning Models ◽  
Content Type ◽  
Financial Distress Prediction ◽  
Distress Prediction ◽  
Gated Recurrent Unit

PurposeIn the previous study of financial distress prediction, deep learning techniques performed better than traditional techniques over time-series data. This study investigates the performance of deep learning models: recurrent neural network, long short-term memory and gated recurrent unit for the financial distress prediction among the Malaysian public listed corporation over the time-series data. This study also compares the performance of logistic regression, support vector machine, neural network, decision tree and the deep learning models on single-year data.Design/methodology/approachThe data used are the financial data of public listed companies that been classified as PN17 status (distress) and non-PN17 (not distress) in Malaysia. This study was conducted using machine learning library of Python programming language.FindingsThe findings indicate that all deep learning models used for this study achieved 90% accuracy and above with long short-term memory (LSTM) and gated recurrent unit (GRU) getting 93% accuracy. In addition, deep learning models consistently have good performance compared to the other models over single-year data. The results show LSTM and GRU getting 90% and recurrent neural network (RNN) 88% accuracy. The results also show that LSTM and GRU get better precision and recall compared to RNN. The findings of this study show that the deep learning approach will lead to better performance in financial distress prediction studies. To be added, time-series data should be highlighted in any financial distress prediction studies since it has a big impact on credit risk assessment.Research limitations/implicationsThe first limitation of this study is the hyperparameter tuning only applied for deep learning models. Secondly, the time-series data are only used for deep learning models since the other models optimally fit on single-year data.Practical implicationsThis study proposes recommendations that deep learning is a new approach that will lead to better performance in financial distress prediction studies. Besides that, time-series data should be highlighted in any financial distress prediction studies since the data have a big impact on the assessment of credit risk.Originality/valueTo the best of authors' knowledge, this article is the first study that uses the gated recurrent unit in financial distress prediction studies based on time-series data for Malaysian public listed companies. The findings of this study can help financial institutions/investors to find a better and accurate approach for credit risk assessment.

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