Predicting infectious disease outbreak impacts on population, healthcare resources and economics and has received a special academic focus during coronavirus (COVID-19) pandemic. Focus on human disease outbreak prediction techniques in current literature, Marques et al. (Predictive models for decision support in the COVID-19 crisis. Springer, Switzerland, 2021) state that there are four main methods to address forecasting problem: compartmental models, classic statistical models, space-state models and machine learning models. We adopt their framework to compare our research with previous works. Besides being divided by methods, forecasting problems can also be divided by the number of variables that are considered to make predictions. Considering this number of variables, forecasting problems can be classified as univariate, causal and multivariate models. Multivariate approaches have been applied in less than 10% of research found. This research is the first attempt to evaluate, over real time-series data of 3 different countries with univariate and multivariate methods to provide a short-term prediction. In literature we found no research with that scope and aim. A comparison of univariate and multivariate methods has been conducted and we concluded that besides the strong potential of multivariate methods, in our research univariate models presented best results in almost all regions’ predictions.

Neural network stochastic differential equation models with applications to financial data forecasting

This paper proposes a clustering approach for multivariate time series with time-varying parameters in a multiway framework. Although clustering techniques based on time series distribution characteristics have been extensively studied, methods based on time-varying parameters have only recently been explored and are missing for multivariate time series. This paper fills the gap by proposing a multiway approach for distribution-based clustering of multivariate time series. To show the validity of the proposed clustering procedure, we provide both a simulation study and an application to real air quality time series data.

By 2025, Belgium will phase-out nuclear power. Unassessed so far, this policy reform may modify the economic and environmental channels through which energy and society interfere in this country. In this paper, we investigate whether this structural energy change may adversely impact the growth of the Belgian economy (i) and its ability to meet its long-term greenhouse gas emission targets (ii). A multivariate model comprising production factors (labor, capital, and exports), nuclear and renewable energy uses, total primary energy supply, economic growth, and CO2 emissions from the power and heating sector is combined with real time-series data spanning the 1974–2019 period. The analysis consists in sequentially assessing two distinct nexuses (energy-economy and energy-economy-environment) over reduced- and augmented frameworks (excluding and including nuclear energy), and through a two-stage empirical strategy: time-series econometric estimations (Toda-Yamamoto causality test, Impulse Response Functions (IRFs), and the Auto-Regressive Distributed Lags (ARDL) and Machine Learning (ML) experiments with a Partial Differential Equations (PDEs) algorithm. For robustness purposes, we conduct two seminal tests which relate to dynamic predictive processes (T-Mat and Verticality tests). Besides confirming the time-series findings, our ML results highlight the necessity to timely manage the process of nuclear phase-out, along with a progressive deployment of installed renewable energy capacity. This should avoid additional economic costs, energy security threats, and undermining of climate targets. In doing so, this study combines macro-level nexus investigations with the politics and institutional determinants of nuclear energy reliance and seeks to bring inclusive knowledge on this topic.

As is being done in most of the world, South Africa has commenced the transition from a fossil fuel-based electricity generation system to one based on renewable sources to meet greenhouse gas emission reduction goals. This paper explores the role of natural gas in South Africa to support the transition to a renewable energy-driven power grid. Specifically, the paper quantifies the firm and dispatchable power requirement to accommodate variability in solar and wind generation sources based on real-time series data from current renewable energy power plants for the country, and demonstrates that natural gas could be one of the elements to meet the medium-term need for this dispatchable power requirement, based on current regional gas resources. A range of alternative natural gas sources are considered in this analysis, covering existing gas resources from Mozambique, deep-water offshore potential from the southern Cape, shale gas from the Karoo basin, as well as liquefied natural gas imports. In addition, the alternatives to natural gas to supply the required dispatchable energy are considered. The analysis shows that the major challenge is to have sufficient gas storage available to be able to provide gas at the very high instantaneous rates required, but where the gas is only used for short periods of time and at low annual rates.

Time-series anomaly detection using dynamic programming based longest common subsequence on sensor data

A solution for the greedy approximation of a step function with a waveform dictionary

Graph theoretic approach to expose the energy-induced crisis in Pakistan

Classical Ornstein–Uhlenbeck (ou) processes are Lévy-driven linear stochastic models with exponentially decaying autocorrelation functions which do not always fit more slowly decaying real time series data. A superposition of ou processes (known as a supou process) is proposed to overcome this issue for application to river discharge time series data. The discharge data has a sub-exponential autocorrelation function and this is captured by the supou process based on the mean reversion speed generated by a Gamma distribution. All the parameters of the supou process are identified by matching the autocorrelation and the first to fourth statistical moments of the discharge data. The empirical and modelled histograms of the discharge data are comparable with each other.

Physiological vital signs acquired during traumatic events are informative on the dynamics of the trauma and their relationship with other features such as sample-specific covariates. Non-time dependent covariates may introduce extra challenges in the Gaussian Process (<img src=image/13425527_01.gif>) regression, as their main predictors are functions of time. In this regard, the paper introduces the use of Orthogonalized Gnanadesikan-Kettering covariates for handling such predictors within the Gaussian process regression framework. Spectral Bayesian <img src=image/13425527_01.gif> regression is usually based on symmetric spectral frequencies and this may be too restrictive in some applications, especially physiological vital signs modeling. This paper builds on a fast non-standard variational Bayes method using a modified Van der Waerden sparse spectral approximation that allows uncertainty in covariance function hyperparameters to be handled in a standard way. This allows easy extension of Bayesian methods to complex models where non-time dependent predictors are available and the relationship between the smoothness of trend and covariates is of interest. The utility of the methods is illustrated using both simulations and real traumatic systolic blood pressure time series data.

While considerable attention has been given to data driven methods that analyse and control energy systems in buildings, the same cannot be said for building water systems. As a result, approaches which support enhanced efficiency in building water consumption are somewhat underdeveloped, particularly in industrial settings. Water consumption in industrial systems features non-stationarity (i.e., variations in statistical properties over time), making it challenging to distinguish between routine and non-routine water uses. In such scenarios, fault detection and diagnosis methods that leverage multivariate statistical process control with, for example, principal component analysis and detection indices (Hotelling T2-statistics and Q-statistics), can be successfully used to identify system alarms. However, even with these approaches there can be a high prevalence of false alarms leading to low industry uptake of fault detection and diagnosis systems, or where in place, alarms can be ignored. To efficiently detect and diagnose water distribution system faults, false alarms should be controlled through false alarm moderation approaches so that building managers/operators only need to focus on critical system alarms or system alarms with high risk levels. This paper utilises two statistical non-parametric false alarm moderation approaches (window-based, and trial-based) that generate a second control limit for T2-statistics and Q-statistics. The implementation of these false alarm moderation approaches was combined with principal component analysis to detect faults with real water time series data from two case-study sites. Using both approaches false alarms were reduced, and the overall performance and reliability of the fault detection and diagnosis approach was improved. The principal component analysis model with the window-based approach was shown to be particularly effective.

Modelling persistent stationary processes in continuous time

The ability to predict the radioactive soil radon gas concentration is important for human beings because it serves as a precursor to earthquakes. Several studies have been conducted across the globe to confirm the correlation of radon emission dynamics and earthquakes, and concluded that the soil radon gas is the witness of anomalous behaviour before the occurrences of several earthquakes. This anomalous behavior can help to construct a better prediction model for earthquake forecasting. This paper aims at employing different ensemble and individual machine learning methods on real time radon time series data with different scenarios to predict anomalies in data caused by the seismic activities.The ensemble methods include boosted tree, bagged cart and boosted linear model while standalone machine learning methods include support vector machine with linear and radial kernels and k-nearest neighbors ( <inline-formula xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink"> <tex-math notation="LaTeX">${K}$ </tex-math></inline-formula> -NN). We tested the methods on a dataset recorded on the fault line located in Muzaffarabad. Time series data was collected over a period ranging from March 1, 2017 to May 11, 2018 including nine(09) earthquakes. The methods are tested in four different settings with 10 times 10 folds cross validation procedure over the time window of 1 to 4. The repeated 10 fold cross validation is performed to reduce the noise in the model performance estimation by replicating the 10 fold cross validation procedure 10 times. Statistical performance evaluation measures viz. root mean square error (RMSE), root mean squared log error (RMSLE), mean absolute percentage error (MAPE), percentage bias (PB), and mean squared error (MSE) have been calculated for the assessment of performance. In setting 1, the support vector machine with radial kernel performs better with the minimum RMSE score of 1381.023 when compared to other prediction models. In setting 3, it can be observed through different performance metrics such as RMSE, the value in the range [1262.864, 1409.616] which is minimum when other prediction models for predicting soil radon gas concentration dataset. For setting 4, the boosted tree model yielded the minimum RMSE and MAPE scores of 1573.174 and 0.056 respectively. Findings of the study shows that boosted tree and support vector machine with radial kernel proved to be better regression models for the prediction of anomalies in soil radon gas concentration during seismic activities. An important finding of this study suggests that by employing boosted tree ensemble method make us able to accurately predict soil radon gas concentration automatically from environmental parameters.

Thanks to their simplicity and interpretable structure, autoregressive processes are widely used to model time series data. However, many real time series data sets exhibit non-linear patterns, requiring nonlinear modeling. The threshold Auto-Regressive (TAR) process provides a family of non-linear auto-regressive time series models in which the process dynamics are specific step functions of a thresholding variable. While estimation and inference for low-dimensional TAR models have been investigated, high-dimensional TAR models have received less attention. In this article, we develop a new framework for estimating high-dimensional TAR models, and propose two different sparsity-inducing penalties. The first penalty corresponds to a natural extension of classical TAR model to high-dimensional settings, where the same threshold is enforced for all model parameters. Our second penalty develops a more flexible TAR model, where different thresholds are allowed for different auto-regressive coefficients. We show that both penalized estimation strategies can be utilized in a three-step procedure that consistently learns both the thresholds and the corresponding auto-regressive coefficients. However, our theoretical and empirical investigations show that the direct extension of the TAR model is not appropriate for high-dimensional settings and is better suited for moderate dimensions. In contrast, the more flexible extension of the TAR model leads to consistent estimation and superior empirical performance in high dimensions.

Data-driven scenario generation of renewable energy production based on controllable generative adversarial networks with interpretability

A new hybrid fuzzy time series model with an application to predict PM10 concentration

Deep learning models have been widely used in prediction problems in various scenarios and have shown excellent prediction effects. As a deep learning model, the long short-term memory neural network (LSTM) is potent in predicting time series data. However, with the advancement of technology, data collection has become more accessible, and multivariate time series data have emerged. Multivariate time series data are often characterized by a large amount of data, tight timeline, and many related sequences. Especially in real data sets, the change rules of many sequences will be affected by the changes of other sequences. The interacting factors data, mutation information, and other issues seriously impact the prediction accuracy of deep learning models when predicting this type of data. On the other hand, we can also extract the mutual influence information between different sequences and simultaneously use the extracted information as part of the model input to make the prediction results more accurate. Therefore, we propose an ATT-LSTM model. The network applies the attention mechanism (attention) to the LSTM to filter the mutual influence information in the data when predicting the multivariate time series data, which makes up for the poor ability of the network to process data. Weaknesses have greatly improved the accuracy of the network in predicting multivariate time series data. To evaluate the model’s accuracy, we compare the ATT-LSTM model with the other six models on two real multivariate time series data sets based on two evaluation indicators: Mean Absolute Error (MAE) and Root Mean Square Error (RMSE). The experimental results show that the model has an excellent performance improvement compared with the other six models, proving the model’s effectiveness in predicting multivariate time series data.

A two-stage procedure in a first-order autoregressive model is considered that investigates the point and the interval estimation of parameters based on the least squares estimator. The two-stage procedure is shown to be as effective as the best fixed-sample-size procedure. In this regard, the significant properties of the procedure, such as asymptotic risk efficiency, asymptotic efficiency, and asymptotic consistency, are established. A Monte Carlo simulation study is conducted to compare the performance of the two-stage procedure and the purely sequential procedure. Finally, real-time series data are considered to illustrate the applicability of the two-stage procedure.

Solid waste management (SWM) is a key administrative unit for managing the urban waste to deliver an eco-friendly environment to the citizens residing in urban cities. Generally, many technologies are implemented and developed by researchers for enhancing the mechanism of SWM and minimizing the waste generation. Yet, the management of waste generation is still a concern. So, here, there is requirement of technology that can involve the individuals for achieving the target reducing the waste. At present, the blockchain technology is an appropriate technology for SWM, as it provides the applications of time tracing activities, secure data transactions, and automatic reward system. In this study, a blockchain-based reward system is proposed to generate the rewards based on real-time series data such as quantity of garbage and level of waste. Furthermore, LoRa-range-based customized sensors are developed for bins to obtain real time information. Moreover, the generated information further transferred to cloud by utilizing LoRa wireless enabled gateway. By the use of flask server, a technique is proposed for integrating real-time data with blockchain via a local network application programming interface (API). A real-time implementation is evaluated on the data to the check the performance efficiency of the proposed approach, where the procedure of automatic reward system is presented in detail.

A multiple model framework based on time series clustering for shale gas well pressure prediction