I'm trying to create an autoencoder for the anomaly detection task, but I'm noticing that even if it performs very well on the training set, it starts to stop recreating half of the test set. I tried with more than 10 models, (LSTM, ConvAE, ConvLSTM) and all of them fails to reconstruct the time series in the same point.

These are the performances on the training set. The blue part is the original time series and the red one is the time series reconstructed by the AE.

enter image description here

These are the performance on the training set. I don't understand why all the models stop performing from that point. Could that means that there are anomalies in that part?

enter image description here

EDIT: I'm updating the question with some details about my dataset and my code: I have a dataset with 30 devices, and for each one I have about 9000 values. The dataset is structured as well:

device1   device2   device3   ....   device30
 0.20      0.35      0.12              0.56
 1.20      2.10      5.75              0.16
 3.20      9.21      1.94              5.12
 5.20      4.32      0.42              9.56
 ....      ....      ....              ....
 7.20      6.21      0.20              -9.56

Since I'm following this guide, I started creating a sequence method to prepare my data for the Conv1D layer:

# Generated training sequences for use in the model.
def create_sequences(values, time_steps=TIME_STEPS):
    output = []
    for i in range(len(values) - time_steps):
        output.append(values[i: (i + time_steps)])
    return np.stack(output)

This is where I create the sequences and normalize the dataset:

# split the train/val/test set
n_features = dataset_sequences.shape[1]
X_train = dataset_sequences[0:3000, :]
X_val = dataset_sequences[3000:6000, :]
X_test = dataset_sequences[6000:9000, :]

# normalize the data
train_mean = X_train.mean()
train_std = X_train.std()
X_train = (X_train - train_mean) / train_std
X_val = (X_val - train_mean) / train_std
X_test = (X_test - train_mean) / train_std

Then, I feed my X_train with shape (3000, 10, 30) to my Conv1D autoencoder:

model = tf.keras.Sequential(
        tf.keras.layers.Input(shape=(X_train.shape[1], X_train.shape[2])),
            filters=64, kernel_size=5, padding="same", strides=1, activation="relu"),
            filters=32, kernel_size=5, padding="same", strides=1, activation="relu"),
            filters=32, kernel_size=5, padding="same", strides=1, activation="relu"),
            filters=64, kernel_size=5, padding="same", strides=1, activation="relu"),
        tf.keras.layers.Conv1DTranspose(filters=30, kernel_size=5, padding="same"),

2 Answers 2


Looking at your graphic there seems to be a change in the series behaviour right about when your models start predicting 0. Since you have had the same issue with several models I would say that this is a data issue.

  • Have you checked looking at these values?
  • Try plotting the 1-period return?
  • Try skipping the parts in the validation set that work and make prediction directly from when the model is starting to fail - or even a bit after i.e X_test[1200:] or X_test[2000:]
  • $\begingroup$ I checked some of the values of the predictions (only the wrong part, from 1500 to 2500) and the data in the original test set are really different: pastebin.com/sNELq7Jg What do you mean by plotting the 1-period return? $\endgroup$
    – Fabio
    Commented Mar 31, 2021 at 8:20
  • $\begingroup$ The difference is stark. Try using sklearn.preprocessing.RobustScaler hich should help with the change of scale. By the 1-d return I meant just checking the time series variation, but it seems that you found where it comes from. $\endgroup$ Commented Mar 31, 2021 at 15:57
  • $\begingroup$ Tried with both RobustScaler and StandardScaler but these are the results: imgur.com/a/TRT1JFk $\endgroup$
    – Fabio
    Commented Mar 31, 2021 at 17:25
  • $\begingroup$ What is your data source? $\endgroup$ Commented Apr 1, 2021 at 11:14

There are no errors in my code, instead I found out that when you work with anomaly detection tasks and you get a reconstruction which is not like the original one, then you just found the anomaly in the dataset.

  • 1
    $\begingroup$ awesome, that's good to know $\endgroup$ Commented Apr 1, 2021 at 11:46

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