70   Artículos

 
en línea
Adriano Mancini, Francesco Solfanelli, Luca Coviello, Francesco Maria Martini, Serena Mandolesi and Raffaele Zanoli    
Yield prediction is a crucial activity in scheduling agronomic operations and in informing the management and financial decisions of a wide range of stakeholders of the organic durum wheat supply chain. This research aims to develop a yield forecasting s... ver más
Revista: Agronomy    Formato: Electrónico

 
en línea
Mingze Li, Bing Li, Zhigang Qi, Jiashuai Li and Jiawei Wu    
Predicting ship trajectories plays a vital role in ensuring navigational safety, preventing collision incidents, and enhancing vessel management efficiency. The integration of advanced machine learning technology for precise trajectory prediction is emer... ver más
Revista: ISPRS International Journal of Geo-Information    Formato: Electrónico

 
en línea
Ilia Zaznov, Julian Martin Kunkel, Atta Badii and Alfonso Dufour    
This paper introduces a novel deep learning approach for intraday stock price direction prediction, motivated by the need for more accurate models to enable profitable algorithmic trading. The key problems addressed are effectively modelling complex limi... ver más
Revista: Applied Sciences    Formato: Electrónico

 
en línea
Styliani Tassiopoulou, Georgia Koukiou and Vassilis Anastassopoulos    
Sunspot daily time series have been available for almost two centuries providing vast and complicated information about the behavior of our star and especially the interaction of the motion of the planets and other possible interstellar phenomena and the... ver más
Revista: Applied Sciences    Formato: Electrónico

 
en línea
Han Lin Shang    
A key summary statistic in a stationary functional time series is the long-run covariance function that measures serial dependence. It can be consistently estimated via a kernel sandwich estimator, which is the core of dynamic functional principal compon... ver más
Revista: Forecasting    Formato: Electrónico

 
en línea
José Francisco Lima, Fernanda Catarina Pereira, Arminda Manuela Gonçalves and Marco Costa    
Linear models, seasonal autoregressive integrated moving average (SARIMA) models, and state-space models have been widely adopted to model and forecast economic data. While modeling using linear models and SARIMA models is well established in the literat... ver más
Revista: Forecasting    Formato: Electrónico

 
en línea
Dimitris Fotakis, Panagiotis Patsilinakos, Eleni Psaroudaki and Michalis Xefteris    
In this work, we consider the problem of shape-based time-series clustering with the widely used Dynamic Time Warping (DTW) distance. We present a novel two-stage framework based on Sparse Gaussian Modeling. In the first stage, we apply Sparse Gaussian P... ver más
Revista: Algorithms    Formato: Electrónico

 
en línea
Yubo Zheng, Yingying Luo, Hengyi Shao, Lin Zhang and Lei Li    
Contrastive learning, as an unsupervised technique, has emerged as a prominent method in time series representation learning tasks, serving as a viable solution to the scarcity of annotated data. However, the application of data augmentation methods duri... ver más
Revista: Applied Sciences    Formato: Electrónico

 
en línea
Zhenwen He, Chi Zhang and Yunhui Cheng    
Time series data typically exhibit high dimensionality and complexity, necessitating the use of specific approximation methods to perform computations on the data. The currently employed compression methods suffer from varying degrees of feature loss, le... ver más
Revista: Algorithms    Formato: Electrónico

 
en línea
Hui Sheng, Min Liu, Jiyong Hu, Ping Li, Yali Peng and Yugen Yi    
Time-series data is an appealing study topic in data mining and has a broad range of applications. Many approaches have been employed to handle time series classification (TSC) challenges with promising results, among which deep neural network methods ha... ver más
Revista: Information    Formato: Electrónico

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