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A time series is a sequence of time-ordered data, and it is generally used to describe how a phenomenon evolves over time. Time series forecasting, estimating future values of time series, allows the implementation of decision-making strategies. Deep learning, the currently leading field of machine learning, applied to time series forecasting can c...
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... λ 1 , λ 2 , λ 3 are the regularisation parameters of divergence between target distribution and distribution of prediction window, denoising score matching objective, and total correlation among latent variables, respectively. Diffusion models for time series forecasting are summarised in Table 11. ...Similar publications
INTRODUCTION: Vulnerability detection is crucial for preventing severe security incidents like hacker attacks, data breaches, and network paralysis. Traditional methods, however, face challenges such as low efficiency and insufficient detail in identifying code vulnerabilities. OBJECTIVES: This paper introduces E-GVD, an advanced method for source...
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... Time series forecasting projects future values in this sequence, supporting informed decision-making strategies. Deep learning, a key component of machine learning, excels at handling complex, high-dimensional time series data beyond the scope of traditional methods [14]. This research explores several deep learning models, including simple recurrent neural networks (RNNs), long short-term memory (LSTM) networks, and gated recurrent units (GRUs), that can be used to forecast PV power generation and EV load demand. ...
The transition from internal combustion engine vehicles to electric vehicles (EVs) is gaining momentum due to their significant environmental and economic benefits. This study addresses the challenges of integrating renewable energy sources, particularly solar power, into EV charging infrastructures by using deep learning models to predict photovoltaic (PV) power generation and EV charging demand. The study determines the optimal battery energy storage capacity and charging schedule based on the prediction result and actual data. A dataset of a 15 kWp rooftop PV system and simulated EV charging data are used. The results show that simple RNNs are most effective at predicting PV power due to their adept handling of simple patterns, while bidirectional LSTMs excel at predicting EV charging demand by capturing complex dynamics. The study also identifies an optimal battery storage capacity that will balance the use of the grid and surplus solar power through strategic charging scheduling, thereby improving the sustainability and efficiency of solar energy in EV charging infrastructures. This research highlights the potential for integrating renewable energy sources with advanced energy storage solutions to support the growing electric vehicle infrastructure.
... Deep learning methods have promising results for forecasting due to their ability to capture complex patterns and relationships in data. Temporal dependencies, nonlinear relationships, and high-dimensional time series data that are challenging for traditional statistical methods can be handled successfully by deep learning methods [30]. Convolutional Neural Networks (CNNs) were applied successfully on electricity price and load forecasting in [31]. ...
Smart cities facilitate the comprehensive management and operation of urban data generated within a city, establishing the foundation for smart services and addressing diverse urban challenges. A smart system for public laundry management uses artificial intelligence-based solutions to solve the challenges of the inefficient utilization of public laundries, waiting times, overbooking or underutilization of machines, balancing of loads across machines, and implementation of energy-saving features. We propose SmartLaundry, a real-time system design for public laundry smart recommendations to better manage the loads across connected machines. Our system integrates the current status of the connected devices and data-driven forecasted usage to offer the end user connected via a mobile application a list of recommended machines that could be used. We forecast the daily usage of devices using traditional machine learning techniques and deep learning approaches, and we perform a comparative analysis of the results. As a proof of concept, we create a simulation of the interaction with our system.
... However, these models have limitations in terms of forecasting accuracy due to their parametric linear nature; and the univariate character of the models may result in missing information from other temporal processes that could improve forecasting. To overcome these limitations, first statistical models such as Vector Autoregression (VAR) and finally Deep Learning (DL) based models enhanced predictivity power with non-linear formulations, also taking into account multivariate data [7]. However, these models are considered opaque in terms of explainability [8]. ...
... In this section, we focus on the three architectures that yield the best results (GRU, LSTM, and BiLSTM) and the three different variable selection methods that have shown the best results: single predictor, Kernel SHAP feature selection, and CCLR-DL proposal. Subsequently, we assess the prediction performance using different parameterizations of look_back = [30,60,90,182,365] and forecast_range = [1,7,14,30,90,182,365]. BiLSTM architecture consistently achieves the best results, followed by GRU, and finally LSTM in the different feature selection scenarios. ...
Hybrid forecasting methods have emerged as a solution surpassing the limitations of both statistical and deep learning approaches. While the first emphasize the significance of variables, they often produce worse forecasting results when compared to newer techniques. In contrast, deep learning models remain enigmatic "black boxes" in terms of interpretability, although achieving better results in forecasting. This article introduces the Comprehensive Cross-Correlation and Lagged Linear Regression Deep Learning (CCLR-DL) framework, designed to harness the best of both approaches, enhancing forecasting accuracy while retaining model interpretability through a feature selection process. CCLR-DL blens cross-correlation analysis, lagged multiple linear regression and granger's causality procedures with deep learning architectures based on LSTM. In a practical demonstration, CCLR-DL was applied to a real database of clinical visits associated to diagnoses in Catalonia, Spain (tracking a population of 6.3 million patients during 10 years). Predicting visits enables the healthcare managers to be ready for future demand shifts. Results demonstrate a consistent and substantial improvement over standalone statistical and deep learning methods when predicting healthcare demand. This hybrid approach not only showcases its efficacy but also offers a promising solution to the challenge of balancing predictive accuracy with model explicability. In this context, this work aims to design and validate a method for feature selection and forecasting of multivariate high dimensional time series datasets not only to improve prediction accuracy but also to model transparency by identifying a subset of variables that improve predictions and G-cause the target variable.
... Time series prediction has important theoretical implications as well as various technical applications. Forecasting is a procedure that determines future values of time series data based on past data and can be utilized in applications where estimation is not feasible [36][37][38][39][40][41][42][43][44][45][46][47]. ...
... Time series forecasting is frequently performed with recurrent neural networks (RNNs), although they have limitations beyond the training horizon. The main issues are as follows [39][40][41]: ...
... A GRU cell demonstrates similarities to both LSTM and RNN cells [39,40]. ...
The power supply is crucial in the present day due to the negative impacts of poor power quality on the electric grid. In this research, we employed deep learning methods to investigate the power factor, which is a significant indicator of power quality. A multi-step forecast was developed for the power factor in the power supply installation of a hot rolling mill, extending beyond the horizontal line. This was conducted using data obtained from the respective electrical supply system. The forecast was developed via hybrid RNN (recurrent neural networks) incorporating LSTM (long short-term memory) and GRU (gated recurrent unit) layers. This research utilized hybrid recurrent neural network designs with deep learning methods to build several power factor models. These layers have advantages for time series forecasting. After conducting time series forecasting, qualitative indicators of the prediction were identified, including the sMAPE (Symmetric Mean Absolute Percentage Error) and regression coefficient. In this paper, the authors examined the quality of applied models and forecasts utilizing these indicators, both in the short term and long term.
... The analysis of sequential data through deep learning has gained significant attention in the recent literature. This approach is particularly useful for tasks such as time series forecasting and estimating future values in time series [15]. Videos are commonly used in tasks such as audiovisual speech recognition, where GANs can be employed in a multimodal manner. ...
In this study, we propose a novel Temporal Development Generative Adversarial Network (TD-GAN) for the generation and analysis of videos, with a particular focus on biological and medical applications. Inspired by Progressive Growing GAN (PG-GAN) and Temporal GAN (T-GAN), our approach employs multiple discriminators to analyze generated videos at different resolutions and approaches. A new Temporal Discriminator (TD) that evaluates the developmental coherence of video content is introduced, ensuring that the generated image sequences follow a realistic order of stages. The proposed TD-GAN is evaluated on three datasets: Mold, Yeast, and Embryo, each with unique characteristics. Multiple evaluation metrics are used to comprehensively assess the generated videos, including the Fréchet Inception Distance (FID), Frechet Video Distance (FVD), class accuracy, order accuracy, and Mean Squared Error (MSE). Results indicate that TD-GAN significantly improves FVD scores, demonstrating its effectiveness in generating more coherent videos. It achieves competitive FID scores, particularly when selecting the appropriate number of classes for each dataset and resolution. Additionally, TD-GAN enhances class accuracy, order accuracy, and reduces MSE compared to the default model, demonstrating its ability to generate more realistic and coherent video sequences. Furthermore, our analysis of stage distribution in the generated videos shows that TD-GAN produces videos that closely match the real datasets, offering promising potential for generating and analyzing videos in different domains, including biology and medicine.
Timely estimation of earthquake magnitude plays a crucial role in the early warning systems for earthquakes. Despite the inherent danger associated with earthquake energy, earthquake research necessitates extensive parameter estimation and predictive techniques to account for uncertain trends in earthquake waveforms when determining earthquake magnitudes using a single station. This study introduces an effective solution to tackle the issue through the automatic magnitude deep network (AMagDN) model. The proposed model includes long short-term memory (LSTM), a bidirectional LSTM, an autocorrelation attention mechanism, and a machine learning block that can capture detailed information from the seismic waveform recorded during an earthquake. The unique feature of this model is the use of multivariate time series waveforms derived from recorded accelerograms specifically tailored to their energy significance with magnitude and seven fusion tabular parameters involving source and geospatial features. The proposed model’s training, validation and testing are done using independent 15014, 1287 and 3448 records maintained by the Kyoshin network, Japan, for moderate to great impact earthquakes between 5.5 and 8.0 (MJMA\documentclass[12pt]{minimal} \usepackage{amsmath} \usepackage{wasysym} \usepackage{amsfonts} \usepackage{amssymb} \usepackage{amsbsy} \usepackage{mathrsfs} \usepackage{upgreek} \setlength{\oddsidemargin}{-69pt} \begin{document}$$M_\textrm{JMA}$$\end{document}). A comparative study shows that the proposed model outperforms recent state-of-the-art models and common linear relations, reducing mean absolute prediction error by 40% from the second-best model. The multi-stations data are also used for successfully forecasting the magnitudes of two significant earthquakes of 7.7 and 7.3 magnitude (MJMA\documentclass[12pt]{minimal} \usepackage{amsmath} \usepackage{wasysym} \usepackage{amsfonts} \usepackage{amssymb} \usepackage{amsbsy} \usepackage{mathrsfs} \usepackage{upgreek} \setlength{\oddsidemargin}{-69pt} \begin{document}$$M_\textrm{JMA}$$\end{document}) using the proposed model. The reliable prediction capabilities of the proposed model for both single and multi-station data clearly demonstrate its utility in reducing earthquake hazards.
This study presents a novel approach for predicting hierarchical short time series. In this article, our objective was to formulate long-term forecasts for household natural gas consumption by considering the hierarchical structure of territorial units within a country’s administrative divisions. For this purpose, we utilized natural gas consumption data from Poland. The length of the time series was an important determinant of the data set. We contrast global techniques, which employ a uniform method across all time series, with local methods that fit a distinct method for each time series. Furthermore, we compare the conventional statistical approach with a machine learning (ML) approach. Based on our analyses, we devised forecasting methods for short time series that exhibit exceptional performance. We have demonstrated that global models provide better forecasts than local models. Among ML models, neural networks yielded the best results, with the MLP network achieving comparable performance to the LSTM network while requiring significantly less computational time.
