Improving Neural Networks for Time Series Forecasting using Data
Augmentation and AutoML
- URL: http://arxiv.org/abs/2103.01992v1
- Date: Tue, 2 Mar 2021 19:20:49 GMT
- Title: Improving Neural Networks for Time Series Forecasting using Data
Augmentation and AutoML
- Authors: Indrajeet Y. Javeri, Mohammadhossein Toutiaee, Ismailcem B. Arpinar,
Tom W. Miller, John A. Miller
- Abstract summary: This paper presents an easy to implement data augmentation method to significantly improve the performance of neural networks.
It shows that data augmentation, when paired Automated Machine Learning techniques such as Neural Architecture Search, can help to find the best neural architecture for a given time series.
- Score: 0.0
- License: http://arxiv.org/licenses/nonexclusive-distrib/1.0/
- Abstract: Statistical methods such as the Box-Jenkins method for time series
forecasting have been prominent since their development in 1970. Many
researchers rely on such models as they can be efficiently estimated and also
provide interpretability. However, advances in machine learning research
indicate that neural networks can be powerful data modeling techniques, as they
can give higher accuracy for a plethora of learning problems and datasets. In
the past, they have been tried on time series forecasting as well, but their
overall results have not been significantly better than the statistical models
especially for intermediate length times series data. Their modeling capacities
are limited in cases where enough data may not be available to estimate the
large number of parameters that these non-linear models require. This paper
presents an easy to implement data augmentation method to significantly improve
the performance of such networks. Our method, Augmented-Neural-Network, which
involves using forecasts from statistical models, can help unlock the power of
neural networks on intermediate length time series and produces competitive
results. It shows that data augmentation, when paired Automated Machine
Learning techniques such as Neural Architecture Search, can help to find the
best neural architecture for a given time series. Using the combination of
these, demonstrates significant enhancement for two configurations of our
technique for COVID-19 dataset, improving forecasting accuracy by 19.90% and
11.43%, respectively, over the neural networks that do not use augmented data.
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