Search

The concept of predictions has gained much attention over the last few years. Research on prediction based on experience is error-prone. Usually, a lot of data has been available with multiple variables, but not all features are relevant. This work uses different feature selection techniques and their combinations to select the best possible feature subset. This paper uses feature selection techniques for time series data prediction with deep learning models. In this work, we have used filter, wrapper, and embedded methods for feature selection. We combine these techniques and compare their performance with single feature selection techniques. We found that the embedded method alone performs well with fewer features, but its performance deteriorates when the number of features increases. We input these reduced feature sets into deep learning models such as recurrent neural networks (RNN), long short-term memory (LSTM), and 1d Convolutional with LSTM (1d CNN-LSTM). We assess the performance of different deep-learning models with reduced features. Our results analysis found that the right feature subset may help increase the performance of deep learning models for time series prediction. Performance improvement is specific to the dataset, feature selection techniques, and the deployed DL models; performance improvement varies on average, from a few percent to fifty percent. [ABSTRACT FROM AUTHOR]

• The paper fills the gap in the literature by reviewing and analyzing the current studies that apply deep learning for Covid-19 forecasting. • The initial search identified 152 studies of which 53 passed the quality control. • The existing literature is categorized using a model-based taxonomy. • The description of the models along with their performance evaluation is presented. • Recommendations for future improvements are provided. The Covid-19 pandemic has galvanized scientists to apply machine learning methods to help combat the crisis. Despite the significant amount of research there exists no comprehensive survey devoted specifically to examining deep learning methods for Covid-19 forecasting. In this paper, we fill the gap in the literature by reviewing and analyzing the current studies that use deep learning for Covid-19 forecasting. In our review, all published papers and preprints, discoverable through Google Scholar, for the period from Apr 1, 2020 to Feb 20, 2022 which describe deep learning approaches to forecasting Covid-19 were considered. Our search identified 152 studies, of which 53 passed the initial quality screening and were included in our survey. We propose a model-based taxonomy to categorize the literature. We describe each model and highlight its performance. Finally, the deficiencies of the existing approaches are identified and the necessary improvements for future research are elucidated. The study provides a gateway for researchers who are interested in forecasting Covid-19 using deep learning. [ABSTRACT FROM AUTHOR]

With the development of modern cities, urban rail transit has become an indispensable part of residents' travelling mode, and accurate prediction of urban rail transit passenger flow is particularly important. However, due to the non-linearity and non-stability of passenger flow, the low quality of big data and the lack of data make it more and more difficult to predict the passenger flow of urban rail transit. In this paper, we propose a unique model structure that ingeniously integrates Gated Recurrent Units (GRU), Convolutional Neural Networks (CNN) with fused residual network connections, and a self-attention mechanism. The design of this model is primarily to effectively handle complex and low-quality data, which is often prevalent in passenger flow prediction scenarios. To validate the effectiveness of the proposed model, we used data from the Beijing urban rail transit in 2016 for prediction. In the experiments, we performed comparison study and ablation study. The experimental results show that the model proposed in this paper has significantly improved prediction accuracy in both horizontal and vertical comparisons. This outcome substantiates that the proposed model can not only effectively handle complex and low-quality data but also extract short-term features and long-term dependency features of passenger flow well, thereby achieving more accurate predictions. • Integration of GRU, CNN with fused residual connections, and self-attention mechanism constitutes a novel model. • The model is capable of handling complex and lower-quality data, significantly improving the accuracy of predictions. • The model can simultaneously extract short-term features and long-term dependency features from time series. [ABSTRACT FROM AUTHOR]

This research paper provides a framework for the efficient representation and analysis of both spatial and temporal dimensions of panel data. This is achieved by representing the data as spatio-temporal image-matrix, and applied to a case study on forecasting public transport ridership. The relative performance of a subset of machine learning techniques is examined, focusing on Convo-lutional Neural Networks (CNN) and Long Short-Term Memory (LSTM) neural networks. Furthermore Sequential CNN-LSTM, Parallel CNN-LSTM, Augmented Sequential CNN-LSTM are explored. All models are benchmarked against a Fixed Effects Ordinary Least Squares regression. Historical ridership data has been provided in the framework of a project focusing on the impact that the opening of a new metro line had on ridership. Results show that the forecasts produced by the Sequential CNN-LSTM model performed best and suggest that the proposed framework could be utilised in applications requiring accurate modelling of demand for public transport. The described augmentation process of Sequential CNN-LSTM could be used to introduce exogenous variables into the model, potentially making the model more explainable and robust in real-life settings. [ABSTRACT FROM AUTHOR]

• The SolarNet generates accurate intra-hour solar forecasts from a single sky image. • The SolarNet outperforms machine learning models under various weather conditions. • The SolarNet has 8.85% forecasting nRMSE and a 25.14% forecasting skill score. The exponential growth of solar energy poses challenges to power systems, mostly due to its uncertain and variable characteristics. Hence, solar forecasting, such as very short-term solar forecasting (VSTSF), has been widely adopted to assist power system operations. The VSTSF takes inputs from various sources, among which sky image-based VSTSF is not yet well-studied compared to its counterparts. In this paper, a deep convolutional neural network (CNN) model, called the SolarNet, is developed to forecast the operational 1-h-ahead global horizontal irradiance (GHI) by only using sky images without numerical measurements and extra feature engineering. The SolarNet has a set of models that generate fixed-step GHI in parallel. Each model is composed of 20 convolutional, max-pooling, and fully-connected layers, which learns latent patterns between sky images and GHI in an end-to-end manner. Numerical results based on six years data show that the developed SolarNet outperforms the benchmarking persistence of cloudiness model and machine learning models with an 8.85% normalized root mean square error and a 25.14% forecasting skill score. The SolarNet shows superiority under various weather conditions. [ABSTRACT FROM AUTHOR]

With the growth of global energy demand, the proportion of the total installed wind capacity continues to increase. Wind speed forecasting is essential to enhance the utilization of wind energy. However, it is not easy to make accurate wind speed forecasting since wind speed time series data has nonlinearity, fluctuation, and intermittence. These features of wind speed time series data make classical models unable to obtain expected forecasting results. Many researchers proposed wind speed forecasting models to overcome the shortage. Nevertheless, some wind speed prediction methods are limited because they cannot mine all implicit features of wind speed data. This paper proposes a novel wind speed forecasting model that can capture all implicit information of wind speed data and obtain accuracy prediction results. To be specific, ensemble empirical mode decomposition (EEMD) is firstly applied to remove the noise in the original wind speed data. Secondly, we build a novel deep learning model based on the attention mechanism and convolutional neural network (CNN). The proposed forecasting model can focus the important part of wind speed data and reduce the high computational complexity of CNN. Finally, a full connect neural network layer is employed to obtain wind speed forecasting results. In order to validate the performance of the proposed model, we take the wind speed data of the M2 tower at 20-meter height of the National Wind Power Technology Center of the United States as an example. The experimental results demonstrate that the forecasting errors of the proposed model are smaller than other comparative models. And the Diebold-Mariano test confirms that the proposed model exhibits a significant difference in performance compared with the comparison models. [ABSTRACT FROM AUTHOR]

Natural gas is vital in the world's energy portfolio and is widely applied to power generation, urban heating, and manufacturing. Forecasting natural gas consumption with high accuracy is thus crucial in order to maintain a reliable supply for various applications. The demand for natural gas often exhibits different seasonal patterns regarding customers of different characteristics. The precision of forecasters will be vulnerably affected without carefully exploring the periodicity of usage. This paper proposes a novel method, Dual Convolution with Seasonal Decomposition Network, for natural gas consumption forecasting. The proposed method applies multiple seasonal-trend decomposition to separate time series into periodic patterns and residual components. In addition, local and global convolution are combined to predict series with significant fluctuations and poor periodicity. Simulations show that on city-level forecasting, the proposed method outperforms state-of-the-art methods in terms of overall prediction accuracy and variation sensitivity regardless of different time intervals. The performance of the method is robust to the forecasting horizon. The method can be deployed in practical circumstances to forecast the natural gas consumption of residential quarters, cities, or even countries in different time spans. • The multiple seasonal-trend decomposition is crucial for extracting regular patterns. • A global and local convolution network is effective in exploring long-short term features. • The autoregressive component improves the input sensitivity of neural networks. • The proposed method outperforms the state-of-the-art on actual data. [ABSTRACT FROM AUTHOR]

Accurate and timely solar forecasts play an increasingly critical role in power systems. Compared to longer forecasting timescales, very short-term solar forecasting has lagged behind in both research and practice. In this paper, we propose deep convolutional neural networks (CNNs) to provide operational intra-hour (10-minute-ahead to 60-minute-ahead) solar forecasts. We develop two CNN structures inspired by a widely-used CNN architecture. The CNNs are tailored to our solar forecasting regression tasks and rely solely on sky image sequences. Case studies based on six years of data (over 150,000 data points) demonstrate that the best CNN model has forecast skill scores of 20%–39% over the naive persistence of cloudiness benchmark, even at these very short timescales. The CNNs also have consistently superior performance when compared to shallow machine learning models with meteorological predictors, where the improvement averages around 7%. The sensitivity analyses show that the sky image length, resolution, and weather conditions have impacts on the deep learning model accuracy. In our intra-hour problem with specific setups, two sky images with a 10-minute 128 × 128 resolution yield the most accurate forecasts. Current limitations, future work, and deployment challenges and solutions are also discussed. • Two end-to-end sky image-based CNNs are proposed for operational solar forecasting. • Experiments with six years of open-source data show the outperformance of 20%–39%. • Sensitivity analyses are performed to give practical suggestions. • Visualization interpretation analyses are performed to interpret the learning process. [ABSTRACT FROM AUTHOR]

• Deep Learning can be applied in financial forecast, improving results of classic models. • Proposed model with architecture based on a CNN-LSTM combination. • Images associated to time series that have static and dynamic information of the data. • CNN-LSTM model to predict the realized volatility for the price of gold. • The proposed model improves the results respect a normal LSTM network and GARCH. Prediction of volatility for different types of financial assets is one of the tasks of greater mathematical complexity in time series prediction, mainly due to its noisy, non-stationary and heteroscedastic structure. On the other hand, gold is an asset of particular importance for hedging and diversification of investment portfolios, and therefore it is important to predict future volatility of this asset. This paper seeks to significantly improve the forecast of gold volatility by combining two deep learning methodologies: short-term memory networks (LSTM) added to convolutional neural networks (specifically a pre-trained VGG16 network). It is important to mention that these types of hybrid architectures have not been used in time series prediction, so it is a completely new approach to solving these types of problems. The CNN-LSTM hybrid model is capable of including images as input which provides a wide variety of information associated with both static and dynamic characteristics of the series. In parallel, different lags of profitability of the series are entered as input, which allows it to learn from the temporal structure. The results show a substantial improvement when this hybrid model is compared to the GARCH and LSTM models. A 37% reduction in MSE is observed compared to the classic GARCH model, and 18% compared to the LSTM model. Finally, the Model Confidence Model (MCS) determines a significant improvement in the prediction of the hybrid model. The fundamental importance of this research lies in the application of a new type of architecture capable of processing various sources of information for any time series prediction task. [ABSTRACT FROM AUTHOR]