  Space weather phenomena such as solar flares, have massive destructive power
when reaches certain amount of magnitude. Such high magnitude solar flare event
can interfere space-earth radio communications and neutralize space-earth
electronics equipment. In the current study, we explorer the deep learning
approach to build a solar flare forecasting model and examine its limitations
along with the ability of features extraction, based on the available
time-series data. For that purpose, we present a multi-layer 1D Convolutional
Neural Network (CNN) to forecast solar flare events probability occurrence of M
and X classes at 1,3,6,12,24,48,72,96 hours time frame. In order to train and
evaluate the performance of the model, we utilised the available Geostationary
Operational Environmental Satellite (GOES) X-ray time series data, ranged
between July 1998 and January 2019, covering almost entirely the solar cycles
23 and 24. The forecasting model were trained and evaluated in two different
scenarios (1) random selection and (2) chronological selection, which were
compare afterward. Moreover we compare our results to those considered as
state-of-the-art flare forecasting models, both with similar approaches and
different ones.The majority of the results indicates that (1) chronological
selection obtain a degradation factor of 3\% versus the random selection for
the M class model and elevation factor of 2\% for the X class model. (2) When
consider utilizing only X-ray time-series data, the suggested model achieve
high score results compare to other studies. (3) The suggested model combined
with solely X-ray time-series fails to distinguish between M class magnitude
and X class magnitude solar flare events. All source code are available at
https://github.com/vladlanda/Low-Dimensional-Convolutional-Neural-Network-For-Solar-Flares-GOES-Time-Series-Classification
