Results 51 to 60 of about 223,849 (180)

A Multi-Method Survey on the Use of Sentiment Analysis in Multivariate Financial Time Series Forecasting

Entropy, 2021
In practice, time series forecasting involves the creation of models that generalize data from past values and produce future predictions. Moreover, regarding financial time series forecasting, it can be assumed that the procedure involves phenomena ...
Charalampos M. Liapis, Aikaterini Karanikola, Sotiris Kotsiantis   +2 more
doaj   +1 more source

Multivariate out-of-sample tests for Granger causality. [PDF]

A time series is said to Granger cause another series if it has incremental predictive power when forecasting it. While Granger causality tests have been studied extensively in the univariate setting, much less is known for the multivariate case. In this
Croux, Christophe, Gelper, Sarah
core   +3 more sources

Forecasting Inflation Using Univariate and Multivariate Time Series

JISR Management and Social Sciences & Economics, 2009
The purpose of the study is to forecast inflation in Pakistan from January to June 2008. This study set out to redress the deficiency and explicitly use of time series techniques solely for forecasting purposes.
Azam Ali, S.M. Husnain Bokhari
doaj  

Multi-Scale Transformer Pyramid Networks for Multivariate Time Series Forecasting

IEEE Access
Multivariate Time Series (MTS) forecasting entails the intricate process of modeling temporal dependencies within historical data records. Transformers have demonstrated remarkable performance in MTS forecasting due to their capability to capture long ...
Yifan Zhang, Rui Wu, Sergiu M. Dascalu, Frederick C. Harris   +3 more
doaj   +1 more source

Multivariate Segment Expandable Encoder-Decoder Model for Time Series Forecasting

IEEE Access
Accurate time series forecasting is critical in a variety of fields, including transportation, weather prediction, energy management, infrastructure monitoring, and finance.
Yanhong Li, David C. Anastasiu
doaj   +1 more source

Neural Network Ensembles for Time Series Prediction [PDF]

, 2007
Rapidly evolving businesses generate massive amounts of time-stamped data sequences and defy a demand for massively multivariate time series analysis. For such data the predictive engine shifts from the historical auto-regression to modelling complex
Gabrys, Bogdan, Ruta, Dymitr
core   +1 more source

Covariance estimation for multivariate conditionally Gaussian dynamic linear models

, 2007
In multivariate time series, the estimation of the covariance matrix of the observation innovations plays an important role in forecasting as it enables the computation of the standardized forecast error vectors as well as it enables the computation of ...
Anderson, Barassi, Barbosa, Carter, Dahlhaus, Dickey, Doucet, Durbin, Fernández, Francq, Frühwirth-Schnatter, Gamerman, Godolphin, Gravelle, Gupta, Harvey, Harvey, Harvey, Harvey, Huerta, Johansen, Kadiyala, Kadiyala, Kitagawa, Litterman, Lütkepohl, Moauro, Ni, Ooms, Pollock, Queen, Quintana, Quintana, Salvador, Salvador, Salvador, Shumway, Sun, Triantafyllopoulos, Triantafyllopoulos, Uhlig, Watkins, West, West, Whittle   +44 more
core   +1 more source

Leveraging Hybrid Deep Learning Models for Enhanced Multivariate Time Series Forecasting

Neural Processing Letters
Time series forecasting is crucial in various domains, ranging from finance and economics to weather prediction and supply chain management. Traditional statistical methods and machine learning models have been widely used for this task.
Amal Mahmoud, Ammar Mohammed
semanticscholar   +1 more source

HDMixer: Hierarchical Dependency with Extendable Patch for Multivariate Time Series Forecasting

AAAI Conference on Artificial Intelligence
Multivariate time series (MTS) prediction has been widely adopted in various scenarios. Recently, some methods have employed patching to enhance local semantics and improve model performance.
Qihe Huang, Lei Shen, Ruixin Zhang, Jiahuan Cheng, Shouhong Ding, Zhengyang Zhou, Yang Wang   +6 more
semanticscholar   +1 more source

Resource Time Series Analysis and Forecasting in Large-Scale Virtual Clusters

Big Data Mining and Analytics
In today’s rapidly evolving internet landscape, prominent companies across various industries face increasingly complex business operations, leading to significant cluster-scale growth.
Yue Lin, Jiamin Wen, Xudong Zhang, Yan Liang, Jianjiang Li   +4 more
doaj   +1 more source