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↑ Time Series Features


Timeseries features are handled as sequence features, with the only difference being that the matrix in the HDF5 preprocessing file uses floats instead of integers.

Since data is continuous, the JSON file, which typically stores vocabulary mappings, isn't needed.

Input Features


Sequence Encoders

Time series encoders are the same as for Sequence Features, with one exception:

Time series features don't have an embedding layer at the beginning, so the b x s placeholders (where b is the batch size and s is the sequence length) are directly mapped to a b x s x 1 tensor and then passed to the different sequential encoders.

The encoder parameters specified at the feature level are:

  • tied (default null): name of another input feature to tie the weights of the encoder with. It needs to be the name of a feature of the same type and with the same encoder parameters.

Example category feature entry in the input features list:

name: timeseries_column_name
type: timeseries
tied: null
    type: parallel_cnn

Passthrough Encoder

graph LR
  A["12\n7\n43\n65\n23\n4\n1"] --> B["Cast float32"];
  B --> C["Aggregation\n Reduce\n Operation"];
  C --> ...;

The passthrough encoder simply transforms each input value into a float value and adds a dimension to the input tensor, creating a b x s x 1 tensor where b is the batch size and s is the length of the sequence. The tensor is reduced along the s dimension to obtain a single vector of size h for each element of the batch. If you want to output the full b x s x h tensor, you can specify reduce_output: null. This encoder is not really useful for sequence or text features, but may be useful for timeseries features, as it allows for using them without any processing in later stages of the model, like in a sequence combiner for instance.

    type: passthrough
    encoding_size: null
    reduce_output: null


  • encoding_size (default: null): The size of the encoding vector, or None if sequence elements are scalars.
  • reduce_output (default: null): How to reduce the output tensor along the s sequence length dimension if the rank of the tensor is greater than 2. Options: last, sum, mean, avg, max, concat, attention, none, None, null.

Output Features

There are no time series decoders at the moment.

If this would unlock an interesting use case for your application, please file a GitHub Issue or ping the Community Discord.