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View Code? Open in Web Editor NEWThe official API of DoubleAdapt (KDD'23), an incremental learning framework for online stock trend forecasting, WITHOUT dependencies on the qlib package.
The official API of DoubleAdapt (KDD'23), an incremental learning framework for online stock trend forecasting, WITHOUT dependencies on the qlib package.
Thank you for the nice work! In the page 34 of your slides, do you want to express
Check out these lines:
pred = pred[:sum(mask_y[:meta_end])]
y_test = y_test[:sum(mask_y[:meta_end])]
They are executed when mask_y is None.
Should we use x_dim instead of factor_num for the in_dim argument in self.teacher_x = FeatureAdapter(factor_num, num_head, temperature) ?
class DoubleAdapt(ForecastModel):
def init(
self, model, factor_num, x_dim=None, lr=0.001, need_permute=False, num_head=8, temperature=10,
):
super().init(
model, x_dim=x_dim, lr=lr, need_permute=need_permute,
)
self.teacher_x = FeatureAdapter(factor_num, num_head, temperature)
...
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