A review of deep learning models for time series prediction
Z Han, J Zhao, H Leung, KF Ma… - IEEE Sensors Journal, 2019 - ieeexplore.ieee.org
In order to approximate the underlying process of temporal data, time series prediction has
been a hot research topic for decades. Developing predictive models plays an important role …
been a hot research topic for decades. Developing predictive models plays an important role …
Parallel spatio-temporal attention-based TCN for multivariate time series prediction
J Fan, K Zhang, Y Huang, Y Zhu, B Chen - Neural Computing and …, 2023 - Springer
As industrial systems become more complex and monitoring sensors for everything from
surveillance to our health become more ubiquitous, multivariate time series prediction is …
surveillance to our health become more ubiquitous, multivariate time series prediction is …
DSTP-RNN: A dual-stage two-phase attention-based recurrent neural network for long-term and multivariate time series prediction
Y Liu, C Gong, L Yang, Y Chen - Expert Systems with Applications, 2020 - Elsevier
Long-term prediction of multivariate time series is still an important but challenging problem.
The key to solve this problem is capturing (1) the spatial correlations at the same time,(2) the …
The key to solve this problem is capturing (1) the spatial correlations at the same time,(2) the …
An introductory study on time series modeling and forecasting
R Adhikari, RK Agrawal - arXiv preprint arXiv:1302.6613, 2013 - arxiv.org
Time series modeling and forecasting has fundamental importance to various practical
domains. Thus a lot of active research works is going on in this subject during several years …
domains. Thus a lot of active research works is going on in this subject during several years …
Chaos control using least‐squares support vector machines
JAK Suykens, J Vandewalle - International journal of circuit …, 1999 - Wiley Online Library
In this paper we apply a recently proposed technique of optimal control by support vector
machines (SVMs) to chaos control. Vapnik's support vector method, which is based on the …
machines (SVMs) to chaos control. Vapnik's support vector method, which is based on the …
Support vector regression with chaos-based firefly algorithm for stock market price forecasting
Due to the inherent non-linearity and non-stationary characteristics of financial stock market
price time series, conventional modeling techniques such as the Box–Jenkins …
price time series, conventional modeling techniques such as the Box–Jenkins …
[图书][B] Statistical pattern recognition
AR Webb - 2003 - books.google.com
Statistical pattern recognition is a very active area of study andresearch, which has seen
many advances in recent years. New andemerging applications-such as data mining, web …
many advances in recent years. New andemerging applications-such as data mining, web …
Time series prediction using support vector machines: a survey
NI Sapankevych, R Sankar - IEEE computational intelligence …, 2009 - ieeexplore.ieee.org
Time series prediction techniques have been used in many real-world applications such as
financial market prediction, electric utility load forecasting, weather and environmental state …
financial market prediction, electric utility load forecasting, weather and environmental state …
[图书][B] Support vector machines: theory and applications
L Wang - 2005 - books.google.com
The support vector machine (SVM) has become one of the standard tools for machine
learning and data mining. This carefully edited volume presents the state of the art of the …
learning and data mining. This carefully edited volume presents the state of the art of the …
Study of ARIMA and least square support vector machine (LS-SVM) models for the prediction of SARS-CoV-2 confirmed cases in the most affected countries
Discussions about the recently identified deadly coronavirus disease (COVID-19) which
originated in Wuhan, China in December 2019 are common around the globe now. This is …
originated in Wuhan, China in December 2019 are common around the globe now. This is …