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I am using relatively small datasets (1000 time series, with 33 datapoints each) with the following default models:
SimpleFFN
DeepAR
MQCNN
DeepSSM
Given my small dataset and the sometimes large neural networks, I am worrying whether there are too many unknowns to solve in my network (e.g. weighs, biases) given my dataset. In short; that unknowns >> dataset.
This results in too little data and too many unknowns and will cause less well trained models. Right?
My question is; is there a minimum size of your dataset required for using these models? And is there a way to estimate the amount of unknowns in the listed algorithms?
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Hi all,
I am using relatively small datasets (1000 time series, with 33 datapoints each) with the following default models:
Given my small dataset and the sometimes large neural networks, I am worrying whether there are too many unknowns to solve in my network (e.g. weighs, biases) given my dataset. In short; that unknowns >> dataset.
This results in too little data and too many unknowns and will cause less well trained models. Right?
My question is; is there a minimum size of your dataset required for using these models? And is there a way to estimate the amount of unknowns in the listed algorithms?
Thank you in advance!
Bram
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