CODEWITHSUNDEEP
machine-learningtime-seriesmetadataautoencoderanomaly-detection
Someone
Someone

Reputation: 213

Time series Autoencoder with Metadata

I'm trying to build an Autoencoder for detecting anomalies in time series data. My approach is based on this tutorial: https://keras.io/examples/timeseries/timeseries_anomaly_detection/

But my data is more complex then this simple tutorial.

I have two different time series, from two sensors and some metadata, like from which machine the time series was recorded.

with a normal MLP network you could have one network for the time series and one for the metadata and merge them in higher layers. But how can you use this data as an input to an Autoencoder? Do you have any ideas, links to tutorials or papers I didn't found?

Upvotes: -1

Views: 584

Answers (2)

eichi94
eichi94

Reputation: 21

I think I understood it. you have to take a look at the input of the .fit() funktion. It is not one array, but there are seperate arrays for seperate categorical datas. additionaly there is the original input (in this case a time series). Because he has so many arrays in the input, he needs to have a corresponding number of input layers. So there is one Input layer for the Timeseries, another for the same time series (It's an autoencoder so x_train works like y_train) and a list of input layers, directly stacked with the embedding layers for the categorical data. after he has all the data in the corresponding Input layers he can concatenate them as you said. by the way, he's using the same list for the decoder to give him additional information. I tried it out and it turns out that it was helpfull to add a dropout layer (high dropout e.g. 0.6) between the additional inputs and the decoder. If you do so, the decoder has to learn from the latent z and not only from the additional data!

hope I could help you =)

Upvotes: 1

eichi94
eichi94

Reputation: 21

in this tutorial you can see a LSTM-VAE where the input time series is somehow concatenated with categorical data: https://github.com/cerlymarco/MEDIUM_NoteBook/tree/master/VAE_TimeSeries

There is an article explayining the code (but not on detail). There you can find the following explanation of the model:

"The encoder consists of an LSTM cell. It receives as input 3D sequences resulting from the concatenation of the raw traffic data and the embeddings of categorical features. As in every encoder in a VAE architecture, it produces a 2D output that is used to approximate the mean and the variance of the latent distribution. The decoder samples from the 2D latent distribution upsampling to form 3D sequences. The generated sequences are then concatenated back with the original categorical embeddings which are passed through an LSTM cell to reconstruct the original traffic sequences."

But sadly I don't understand exactly how they concatenate the input datas. If you understand it it would be nice if you could explain it =)

Upvotes: 1

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