Working paper, 2020, ENG

Deep learning for time series

Martinelli M.

CNR-ISTI, Pisa, Italy

The main purpose of this technical report is only to introduce to the use of the "pandas" open source, BSD-licensed library1. and of the TensorFlow framework2 fot forecasting time series. The use case chosen is of great relevance, the application is a mere experiment that a posteriori can find a confirmation: data is used for SARS Cov-2 spread forecasting too, it is fair to reiterate that these are only a kind of study notes. This is not an epidemiological study, moreover forecasts are strongly based on the numbero suspected cases, that is most probably higher than reported, of course on the behaviour of citizens, and on other unpredicatble factors. Moreover a recent study3 supposes that most infective people are asympthomatic.

Keywords

Deep learning

CNR authors

Martinelli Massimo

CNR institutes

ISTI – Istituto di scienza e tecnologie dell'informazione "Alessandro Faedo"

ID: 418482

Year: 2020

Type: Working paper

Creation: 2020-03-17 01:08:47.000

Last update: 2021-01-05 18:43:02.000

External links

OAI-PMH: Dublin Core

OAI-PMH: Mods

OAI-PMH: RDF

External IDs

CNR OAI-PMH: oai:it.cnr:prodotti:418482