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Comments (3)

nkchem09 avatar nkchem09 commented on August 17, 2024

Thanks for making this work open-source.
I have tried to replicate your results using Taiwanese datasets.
I have generated images with dimension size of 50 and a period size of 20.
I have done testing with a random forest classifier.
I have tried a different number of estimators to check the improvement of the result.
I get accuracy around 0.55 with the shared Taiwanese dataset.
I would like to know whether you have a guess about what I could be missing?
I would like to know also why there is such a big gap between my results using the exact same pipeline and the results mentioned in the paper.

Hi, I got a problem just the same.
The accuracy result calculated by deepCNN is also aound 0.55. Do you got the key to increase the accuracy result?
Thank you very much.

from going-deeper-with-convolutional-neural-network-for-stock-market-prediction.

yc-wang00 avatar yc-wang00 commented on August 17, 2024

Thanks for making this work open-source.
I've tried different models on the dataset but the accuracy is just overfitting by choosing all up/down. I would like to know whether you have a guess about what I could be missing?

from going-deeper-with-convolutional-neural-network-for-stock-market-prediction.

yc-wang00 avatar yc-wang00 commented on August 17, 2024

Thanks for making this work open-source. I have tried to replicate your results using Taiwanese datasets. I have generated images with dimension size of 50 and a period size of 20. I have done testing with a random forest classifier. I have tried a different number of estimators to check the improvement of the result. I get accuracy around 0.55 with the shared Taiwanese dataset. I would like to know whether you have a guess about what I could be missing? I would like to know also why there is such a big gap between my results using the exact same pipeline and the results mentioned in the paper.

I got the same issue here. Have you resolved the issue?

from going-deeper-with-convolutional-neural-network-for-stock-market-prediction.

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