matlab - Types of Neural Networks to compare for stock prediction -



matlab - Types of Neural Networks to compare for stock prediction -

i doing project on stock cost predictions using neural networks. intend compare 3 different neural networks using same info inputs (historical info , technical indicators) , predict next day closing stock cost using different nns.

i know multi-layer perceptron (mlp) basic 2 other artificial neural networks should compare , why? help point me in right direction appreciated. thanks!

i using matlab project if available source codes start can @ code , seek understand happening good

solution focus disambiguition:

focus on ann-types:

if demo-project intent focused on demonstrating different "technology" used within ann-based predictor(s), there feasible academic sources finding 3 different types -- { perceptrons, rbm, auto-encoders, recurrent-nns, deeply-recurrent } -- tested wish raw results published. warned, computational resources of types need not available straightforward. take care according time, level-of-detail plan address , budget.

focus on quality of ann-training process:

if project aims @ comparing of respective progress various ann have during process of ann-training ( convergence, sensitivity bias / overfitting, ability / grade of generalisation ), there no need spend lot of efforts on ann-design, need adequate amount of data[m,n] optimiser-based framework fine-tune experimental ann's parametrisation. remember computational complexity of problem ... o( f(n) ) ... fighting on optimiser parametrisation state-space vs. o( f(n) ) on actual scale of data[m,n+s]

focus on quality of predictions produced in forward-testing ( on out-of-sample info ):

if project aims @ achieving more complex quantitative comparing of predictors' output, there more of import aspects, type-of-neural-network.

ann-outputlayer design out[j] ( shall intended predictor's output? ) input data[m,n] cleansing[m<m,n] & sanity missing & erroneous data-elements handling features[n<n] selection & synthetic features[s] construction ... i.e. feature-engineering ann-inputlayer design in[n+s] ann-architecture design { in[n+s], hidden[xthlayer][varnodes], out[j] } ann-architecture connectivity model

matlab neural-network

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