Time series Timeseries Deep Learning Machine Learning Python Pytorch fastai | State-of-the-art Deep Learning library for Time Series and Sequences in Pytorch / fastai
from tsai.basics import *
from tsai.learner import all_arch_names
X=torch.rand(8,2,50)
y=torch.rand(8,1)
splits = TimeSplitter(show_plot=False)(y)
fail_test = []
for arch in all_arch_names:
if not "plus" in arch.lower(): continue
try:
fcst = TSForecaster(X, y, splits=splits, arch=arch, metrics=mse)
with ContextManagers([fcst.no_bar(), fcst.no_logging()]):
fcst.fit_one_cycle(1, 1e-3)
except Exception as e:
fail_test.append(arch)
print(arch, e)
test_eq(fail_test, [])
Need to confirm if it's possible to update them so that they can be used with multidimensional targets.
All these architectures fail the following test:
Need to confirm if it's possible to update them so that they can be used with multidimensional targets.