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 …
Practical options for selecting data-driven or physics-based prognostics algorithms with reviews
This paper is to provide practical options for prognostics so that beginners can select
appropriate methods for their fields of application. To achieve this goal, several popular …
appropriate methods for their fields of application. To achieve this goal, several popular …
Anomaly detection in univariate time-series: A survey on the state-of-the-art
M Braei, S Wagner - arXiv preprint arXiv:2004.00433, 2020 - arxiv.org
Anomaly detection for time-series data has been an important research field for a long time.
Seminal work on anomaly detection methods has been focussing on statistical approaches …
Seminal work on anomaly detection methods has been focussing on statistical approaches …
Conditional time series forecasting with convolutional neural networks
We present a method for conditional time series forecasting based on an adaptation of the
recent deep convolutional WaveNet architecture. The proposed network contains stacks of …
recent deep convolutional WaveNet architecture. The proposed network contains stacks of …
Deep learning for time-series analysis
JCB Gamboa - arXiv preprint arXiv:1701.01887, 2017 - arxiv.org
In many real-world application, eg, speech recognition or sleep stage classification, data are
captured over the course of time, constituting a Time-Series. Time-Series often contain …
captured over the course of time, constituting a Time-Series. Time-Series often contain …
Forecast methods for time series data: a survey
Z Liu, Z Zhu, J Gao, C Xu - Ieee Access, 2021 - ieeexplore.ieee.org
Research on forecasting methods of time series data has become one of the hot spots. More
and more time series data are produced in various fields. It provides data for the research of …
and more time series data are produced in various fields. It provides data for the research of …
A novel hybridization of artificial neural networks and ARIMA models for time series forecasting
Improving forecasting especially time series forecasting accuracy is an important yet often
difficult task facing decision makers in many areas. Both theoretical and empirical findings …
difficult task facing decision makers in many areas. Both theoretical and empirical findings …
Forecasting with artificial neural networks:: The state of the art
Interest in using artificial neural networks (ANNs) for forecasting has led to a tremendous
surge in research activities in the past decade. While ANNs provide a great deal of promise …
surge in research activities in the past decade. While ANNs provide a great deal of promise …
An artificial neural network (p, d, q) model for timeseries forecasting
Artificial neural networks (ANNs) are flexible computing frameworks and universal
approximators that can be applied to a wide range of time series forecasting problems with a …
approximators that can be applied to a wide range of time series forecasting problems with a …
Neural networks for the prediction and forecasting of water resources variables: a review of modelling issues and applications
Artificial Neural Networks (ANNs) are being used increasingly to predict and forecast water
resources variables. In this paper, the steps that should be followed in the development of …
resources variables. In this paper, the steps that should be followed in the development of …