cauchyturing / imaging-time-series-to-improve-classification-and-imputation Goto Github PK
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Python 3.6+ (only)
This is a support page of the papers **"Imaging time-series to improve classification and imputation." ** Zhiguang Wang and Tim Oates. In Proceedings of the 24th International Conference on Artificial Intelligence (IJCAI'15), AAAI Press 3939-3945. **"Encoding Time Series as Images for Visual Inspection and Classification Using Tiled Convolutional Neural Networks." ** Wang, Zhiguang, and Tim Oates. In Workshops at the Twenty-Ninth AAAI Conference on Artificial Intelligence. 2015. Pleaes see the wiki page for more details.
Getting error in line number 62 inside the serie21MTF.py file.
line 62 return np.array(MSM), label, q.levels
AttributeError: 'Categorical' object has no attribute 'levels'
need to use this code for generating MTF. Please help me
I have compatibility issue
Can you indicate which pandas and numpy versions you used?
Thx
Hi, My python version is 3.4.4. when run your code ,there are error :
missing patentheses in call to print
print "in window_time_series",series
def paaMarkovMatrix(paalist, levels):
# Generate Markov Matrix given a spesicif number of quantile bins
paaindex = []
for each in paalist:
for level in levels:
if level.left <= each <= level.right:
paaindex.append(k)
return paaindex
what does the variable 'k' means?
Hi, the code missing mpl_toolkits.axes_grid1
from mpl_toolkits.axes_grid1 import make_axes_locatable
Hi,
In the paper, your mention of the Gramian Angular Field and its unique inverse map in the polar coordinate system.
I was wondering if it would be possible for you to provide any code or guidance regarding the implementation of the inverse Gramian Angular Field.
Best regards,
Jiping
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