Exploring the Use of Recurrent Neural Networks for Time Series Forecasting


Authors : Jasmin Praful Bharadiya

Volume/Issue : Volume 8 - 2023, Issue 5 - May

Google Scholar : https://bit.ly/3TmGbDi

Scribd : https://t.ly/FvRS

DOI : https://doi.org/10.5281/zenodo.8020803

Recurrent Neural Networks (RNNs) have become competitive forecasting methods, as most notably shown in the winning method of the recent M4 competition. However, established statistical models such as exponential smoothing (ETS) and the autoregressive integrated moving average (ARIMA) gain their popularity not only from their high accuracy, but also because they are suitable for non-expert users in that they are robust, efficient, and automatic. In these areas, RNNs have still a long way to go. We present an extensive empirical study and an open-source software framework of existing RNN architectures for forecasting, and we develop guidelines and best practices for their use. Recurrent neural networks have been effectively used to predict outcomes from irregular time series data in a variety of industries, including medicine, traffic monitoring, environmental monitoring, and human activity detection. The paper focuses on two widely used methods for dealing with irregular time series data: missing value imputation during the data pre-processing stage and algorithm modification to deal with missing values directly during the learning process. Models that can handle problems with irregular time series data are the only ones that are reviewed; a wider variety of models that deal more widely with sequences and regular time series are not included.

Keywords : Time Series Forecasting, Recurrent Neural Networks, Deep-Latent Variable Models, Sensitivity Analysis and Time Series Data Prediction.

CALL FOR PAPERS


Paper Submission Last Date
31 - March - 2024

Paper Review Notification
In 1-2 Days

Paper Publishing
In 2-3 Days

Video Explanation for Published paper

Never miss an update from Papermashup

Get notified about the latest tutorials and downloads.

Subscribe by Email

Get alerts directly into your inbox after each post and stay updated.
Subscribe
OR

Subscribe by RSS

Add our RSS to your feedreader to get regular updates from us.
Subscribe