def forward(self, tensor_x, tensor_y):
if tensor_x.dim() < tensor_y.dim():
tensor_x = tensor_x.expand_as(tensor_y)
residual = calc_residual3d(tensor_x,
tensor_y,
window=self.window_train,
keep_first_nan=True)
residual = residual.squeeze(-1).transpose(0, 1)
return tsf.rolling_mean(residual, self._window)
def forward(self, tensor):
bias_ts = tensor / tsf.rolling_mean(tensor, window=self._window) - 1
return bias_ts
def forward(self, high_ts):
cond = tsf.rolling_mean(high_ts, self.window) < high_ts
zeros = torch.zeros(high_ts.size())
output_tensor = torch.where(cond, (-1 * tsf.diff(high_ts, 2)), zeros)
return output_tensor
def forward(self, close_ts):
output_tensor = (close_ts - tsf.rolling_mean_(close_ts, self.window)) / tsf.rolling_mean(close_ts,
self.window) * 100
return output_tensor
def forward(self, tensor):
return tsf.rolling_mean(tensor, window=self._window) / tsf.rolling_std(
tensor, window=self._window)
def forward(self, close_ts):
output_tensor = tsf.rolling_mean(close_ts, 12) / close_ts
return output_tensor