Deep Transformer Models for Time Series Forecasting: The Influenza Prevalence Case

Detecting strange attractors in turbulence

Takens, F · 1981

Earlier work this paper cites.

A learning algorithm for continually running fully recurrent neural networks

Williams, R. J. and Zipser, D · 1989

Earlier work this paper cites.

Nonlinear forecasting as a way of distinguishing chaos from measurement error in time series

Sugihara, G. and May, R. M · 1990

Earlier work this paper cites.

Long short-term memory

Hochreiter, S. and Schmidhuber, J · 1997

Earlier work this paper cites.

Detecting influenza epidemics using search engine query data

Ginsberg, J., Mohebbi, M. H., Patel, R. S., Brammer, L., Smolinski, M. S., and Brilliant, L · 2009

Earlier work this paper cites.

Time Series Analysis by State Space Methods: Second Edition

Durbin, J. and Koopman, S. J · 2012

Earlier work this paper cites.

Reassessing google flu trends data for detection of seasonal and pandemic influenza: a comparative epidemiological study at three geographic scales

Olson, D. R., Konty, K. J., Paladini, M., Viboud, C., and Simonsen, L · 2013

Earlier work this paper cites.

Using google flu trends data in forecasting influenza-like-illness related ed visits in omaha, nebraska

Araz, O. M., Bentley, D., and Muelleman, R. L · 2014

Earlier work this paper cites.

The parable of google flu: Traps in big data analysis

Lazer, D., Kennedy, R., King, G., and Vespignani, A · 2014

Cited alongside, same era.

Wikipedia usage estimates prevalence of influenza-like illness in the united states in near real-time

McIver, D. J. and Brownstein, J. S · 2014

Cited alongside, same era.

Twitter improves influenza forecasting

Paul, M. J., Dredze, M., and Broniatowski, D · 2014

Cited alongside, same era.

What can digital disease detection learn from (an external revision to) google flu trends?

Santillana, M., Zhang, D. W., Althouse, B. M., and Ayers, J. W · 2014

Cited alongside, same era.

Neural machine translation by jointly learning to align and translate

Bahdanau, D., Cho, K., and Bengio, Y · 2015

Cited alongside, same era.

Adam: A method for stochastic optimization

Kingma, D. P. and Ba, J · 2015

Estimates of global seasonal influenza-associated respiratory mortality: a modelling study

Iuliano, A. D., Roguski, K. M., Chang, H. H., Muscatello, D. J., Palekar, R., Tempia, S., Cohen, C., Gran, J. M., Schanzer, D., Cowling, B. J., Wu, P., Kyncl, J., Ang, L. W., Park, M., Redlberger-Fritz, M., Yu, H., Espenhain, L., Krishnan, A., Emukule, G., van Asten, L., Pereira da Silva, S., Aungkulanon, S., Buchholz, U., Widdowson, M.-A., Bresee, J. S., and Network, G. S. I.-a. M. C · 2018

Later among the works it cites.

Lstm recurrent neural networks for influenza trends prediction

Liu, L., Han, M., Zhou, Y., and Wang, Y · 2018

Later among the works it cites.

Economic burden of seasonal influenza in the united states

Putri, W. C., Muscatello, D. J., Stockwell, M. S., and Newall, A. T · 2018

Later among the works it cites.

Sequence to sequence with attention for influenza prevalence prediction using google trends

Kondo, K., Ishikawa, A., and Kimura, M · 2019

Later among the works it cites.

A novel data-driven model for real-time influenza forecasting

Venna, S. R., Tavanaei, A., Gottumukkala, R. N., Raghavan, V. V., Maida, A. S., and Nichols, S · 2019

Later among the works it cites.