Non-autoregressive neural machine translation (NAMT) has received increasing attention recently in virtue of its promising acceleration paradigm for fast decoding. However, these splendid speedup gains are at the cost of accuracy, in comparison to it...
Pedestrian trajectory prediction is an important task in practical applications such as automatic driving and surveillance systems. It is challenging to effectively model social interactions among pedestrians and capture temporal dependencies. Previo...
Theinference stage can be accelerated significantly using a Non-Autoregressive Transformer (NAT). However, the training objective used in the NAT model also aims to minimize the loss between the generated words and the golden words in the reference....
Ocean forecasting is critical for various applications and is essential for understanding air–sea interactions, which contribute to mitigating the impacts of extreme events. While data-driven forecasting models have demonstrated considerable po...
A crucial element of computer-assisted pronunciation training systems (CAPT) is the mispronunciation detection and diagnostic (MDD) technique. The provided transcriptions can act as a teacher when evaluating the pronunciation quality of finite speech...
This article explores the fitting of Autoregressive (AR) and Threshold AR (TAR) models with a non-Gaussian error structure. This is motivated by the problem of finding a possible probabilistic model for the realized volatility. A Gamma random error i...
In recent years, deep-learning-based speech synthesis has garnered substantial attention, achieving remarkable advancements in generating human-like speech. However, synthesized speech often lacks naturalness, primarily because models excessively dep...
The construction of knowledge graphs in cyber threat intelligence (CTI) critically relies on automated entity–relation extraction. However, sequence tagging-based methods for joint entity–relation extraction are affected by the order-depe...
We present an any-to-one voice conversion (VC) system, using an autoregressive model and LPCNet vocoder, aimed at enhancing the converted speech in terms of naturalness, intelligibility, and speaker similarity. As the name implies, non-parallel any-t...
Currently, most of the existing link parameter prediction schemes assume that the link state remains constant during the measurement period, making it difficult to capture their time-varying characteristics. To solve this problem, this paper proposes...
Exploring the relationship between nighttime light and land use is of great significance to understanding human nighttime activities and studying socioeconomic phenomena. Models have been studied to explain the relationships, but the existing studies...
It is well known that in a vector autoregressive (VAR) model Granger non-causality is characterized by a set of restrictions on the VAR coefficients. This characterization has been derived under the assumption of non-singularity of the covariance mat...
The core challenge of speech synthesis technology is how to convert text information into an audible audio form to meet the needs of users. In recent years, the quality of speech synthesis based on end-to-end speech synthesis models has been signific...
This study explores the forecasting ability of two powerful non-linear computational methods: artificial neural networks and genetic programming. We use as a case of study the monthly international tourism demand in Spain, approximated by the number...
In recent years, the contrast against energy waste and pollution has become mandatory and widely endorsed. Among the many actors at stake, the building sector energy management is one of the most critical. Indeed, buildings are responsible for 40...
Multiscale estimation for geographically weighted regression (GWR) and the related models has attracted much attention due to their superiority. This kind of estimation method will not only improve the accuracy of the coefficient estimators but also...
This paper focuses on an important issue regarding the forecasting of the hourly energy consumption in the case of large electricity non-household consumers that account for a significant percentage of the whole electricity consumption, the accurate...
Pioneering remote sensing image captioning (RSIC) works use autoregressive decoding for fluent and coherent sentences but suffer from high latency and high computation costs. In contrast, non-autoregressive approaches improve inference speed by predi...
As a typical sequence to sequence task, sign language production (SLP) aims to automatically translate spoken language sentences into the corresponding sign language sequences. The existing SLP methods can be classified into two categories: autoregre...
Forecasting is one of the most growing areas in most sciences attracting the attention of many researchers for more extensive study. Therefore, the goal of this study is to develop an integrated forecasting methodology based on an Artificial Neural N...
This study examines the short- and long-term effects of multiple quantiles of forward exchange rate premiums (FERPs) and COVID-19 cases on the quantiles of stock market returns (SMRs). We extend the Quantile Autoregressive Distributive Lag (QARDL) mo...
This study investigates the use of a non-linear autoregressive exogenous neural network (NARX) model to investigate the nexus between energy usability, economic indicators, and carbon dioxide (CO2) emissions in four Association of South East Asian Na...
This paper focusses on the impact of the COVID-19 on the Stock Exchange of Mauritius (SEM) by modelling the number of daily stock transactions of two banks. Hence, a non-stationary bivariate integer-valued autoregressive and moving average of order 1...
The symmetrical relationship between currency and equity markets has gained much attention among academicians and policy makers in the recent era. Many studies conducted on this relationship have concluded that there is short-run relationship between...
A significant amount of available research employs panel data analysis to evaluate the association between foreign direct investment, the quality of institutions, and human development parameters, meaning that there are currently relatively few studi...
The efficient monitoring and tracking of groundwater level changes are critical for the sustainable management of water resources, especially in light of population growth and climate change. This study evaluates the ability of the Non-linear Autoreg...
In this paper, a Bayesian analysis of finite mixture autoregressive (MAR) models based on the assumption of scale mixtures of skew-normal (SMSN) innovations (called SMSN–MAR) is considered. This model is not simultaneously sensitive to outliers...
Timely diagnosis and prognosis based on degradation symptoms are essential steps for condition-based maintenance (CBM) to guarantee industrial safety and productivity. Most industrial machines operate under variable operating conditions. This time-va...
In the extant literature, there are numerous discussions on China’s environmental sustainability. However, few scholars have considered renewable energy consumption and trade policy simultaneously to debate environmental sustainability. Therefo...
The environmental issues that have arisen as a result of brisk economic expansion have evolved into a barrier to the process of social development. Based on this background, this article investigates the consequences of economic development, energy c...
The purpose of this study was to analyze the irregular pattern of changing inflation as a result of the pass-through of the exchange rate and fluctuations in oil prices in the current globalization scenario. We used annual data sets for crude oil pri...
In this paper, we report a study having as a main goal the obtaining of a method that can provide an accurate forecast of the residential electricity consumption, refining it up to the appliance level, using sensor recorded data, for residential smar...
This study examines the symmetric and asymmetric impacts of international trade on consumption-based carbon emissions (CBEs) in the People’s Republic of China (PRC) and the United States of America (USA) from 1990 to 2018. The analysis uses aut...
Due to severe climate change impact on electricity consumption, as well as new trends in smart grids (such as the use of renewable resources and the advent of prosumers and energy commons), medium-term and long-term electricity load forecasting has b...
Load forecasting of a micro-grid system has become a challenging task due to its high volatile nature and uncertainty. Residential energy consumption is one of the most talked-about and confusing topics among different electricity loads in terms of f...
This study proposes an extension of Seasonal Autoregressive Integrated Moving Average models with exogenous regressors (SARIMAX) by incorporating skew-normal and zero-inflated skew-normal error structures to better accommodate asymmetry and excess ze...
South Africa’s coal consumption accounts for 69.6% of the total energy consumption of South Africa, and this represents more than 88% of African coal consumption, taking the first place in Africa. Thus, predicting the coal demand is necessary,...
An accurate forecast of the electricity consumption is particularly important to both consumers and system operators. The purpose of this study is to develop a forecasting method that provides such an accurate forecast of the month-ahead hourly elect...
Stochastic streamflow synthesis has long been the cornerstone of water resource planning, enabling the generation of extended hydrological sequences that reflect natural variability beyond the limitations of observed records. This paper presents a co...
The paper conducts a literature review of applications of autoregressive methods to short-term forecasting of power demand. This need is dictated by the advancement of modern forecasting methods and their achievement in good forecasting efficiency in...
This paper addresses the problem of energy consumption prediction using neural networks over a set of public buildings. Since energy consumption in the public sector comprises a substantial share of overall consumption, the prediction of such consump...
This paper considers observation driven models with conditional mean and variance dynamics for non-negative valued time series. The motivation is to relax the restriction imposed on the higher order moment dynamics in standard multiplicative error mo...
A convolutional neural network (CNN) transducer decoder was proposed to reduce the decoding time of an end-to-end automatic speech recognition (ASR) system while maintaining accuracy. The CNN of 177 k parameters and a kernel size of 6 generates the p...
In this paper, we present LJ-TTS, a large-scale single-speaker dataset of real and synthetic speech designed to support research in text-to-speech (TTS) synthesis and analysis. The dataset builds upon high-quality recordings of a single English speak...
Most current research in Tibetan speech synthesis relies primarily on autoregressive models in deep learning. However, these models face challenges such as slow inference, skipped readings, and repetitions. To overcome these issues, we propose an enh...
Weather disturbances pose a significant challenge when estimating the energy production of photovoltaic panel systems. Energy production and forecasting models have recently been used to improve energy estimations and maintenance tasks. However, thes...
This paper proposes a family of first order bivariate integer-valued autoregressive (BINAR(1)) with Poisson Lindley innovations (BINAR(1)PL). The model parameters are estimated using the conditional maximum likelihood (CML) estimation approach. The p...
Recurrent Neural Networks (RNN) are basically used for applications with time series and sequential data and are currently being used in embedded devices. However, one of their drawbacks is that RNNs have a high computational cost and require the use...
Traffic flow is used as an essential indicator to measure the performance of the road network and a pivotal basis for road classification. However, the combined prediction model of traffic flow based on seasonal characteristics has been given little...
In recent years, deep learning-based speech synthesis has attracted a lot of attention from the machine learning and speech communities. In this paper, we propose Mixture-TTS, a non-autoregressive speech synthesis model based on mixture alignment mec...
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