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Space weather phenomena such as solar flares, have massive destructive powerwhen reaches certain amount of magnitude. Such high magnitude solar flare eventcan interfere space-earth radio communications and neutralize space-earthelectronics equipment. In the current study, we explorer the deep learningapproach to build a solar flare forecasting model and examine its limitationsalong with the ability of features extraction, based on the availabletime-series data. For that purpose, we present a multi-layer 1D ConvolutionalNeural Network (CNN) to forecast solar flare events probability occurrence of Mand X classes at 1,3,6,12,24,48,72,96 hours time frame. In order to train andevaluate the performance of the model, we utilised the available GeostationaryOperational Environmental Satellite (GOES) X-ray time series data, rangedbetween July 1998 and January 2019, covering almost entirely the solar cycles23 and 24. The forecasting model were trained and evaluated in two differentscenarios (1) random selection and (2) chronological selection, which werecompare afterward. Moreover we compare our results to those considered asstate-of-the-art flare forecasting models, both with similar approaches anddifferent ones.The majority of the results indicates that (1) chronologicalselection obtain a degradation factor of 3\% versus the random selection forthe M class model and elevation factor of 2\% for the X class model. (2) Whenconsider utilizing only X-ray time-series data, the suggested model achievehigh score results compare to other studies. (3) The suggested model combinedwith solely X-ray time-series fails to distinguish between M class magnitudeand X class magnitude solar flare events. All source code are available athttps://github.com/vladlanda/Low-Dimensional-Convolutional-Neural-Network-For-Solar-Flares-GOES-Time-Series-Classification

Low Dimensional Convolutional Neural Network For Solar Flares GOES Time Series Classification