Comments (3)
Do I have to reshape my X_train to (1, 5210,6)?
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This model aims at clustering whole multivariate time series, i.e. the input matrix should be (N, T, F) where N is the number of input series, T the length of each series (timesteps), and F the number of observed variables (features). The clustering is performed on the N inputs. I think your data set does not meet this requirement. What do you want to cluster exactly? The 5210 individual time points (i.e. 5210 points in a 6-dimensional space)? or the 6 univariate time series (i.e. 6 points in a 5210-dimensional space)?
from deeptemporalclustering.
Hello Florent,
Thanks for the response. I'm actually trying to classify 5210 points in a 6-dimensional space. I'm trying to see if I can get a Machine Learning algorithm can classify up trends, down trends and neutral trends in Stock market data.
After looking more carefully at the code and the input data used I realized what is going on.
Thanks and regards.
from deeptemporalclustering.
Related Issues (20)
- Dimension Reduction HOT 7
- Assertion error HOT 5
- Heatmap HOT 3
- Nan: Predicted Value HOT 2
- ValueError: The name "reshape" is used 2 times in the model. All layer names should be unique. HOT 3
- CuDNNLSTM not found HOT 3
- Training and Validation Losses HOT 1
- Problem with Autoencoder Dimensions HOT 2
- Heatmap use HOT 2
- input shape HOT 2
- how to load model.h5 HOT 2
- About the loss value HOT 2
- Heatmap issue HOT 4
- Loss interpretation
- Agglomerative Clustering without n_clusters HOT 1
- Dependency Problems with cudnn and Tensorflow HOT 1
- Practical Use
- Requirements are hard to find out HOT 1
- variable time step HOT 4
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