Utility function for generating batches of temporal data.

    Utility function for generating batches of temporal data.

    timeseries_generator(
      data,
      targets,
      length,
      sampling_rate = 1,
      stride = 1,
      start_index = 0,
      end_index = NULL,
      shuffle = FALSE,
      reverse = FALSE,
      batch_size = 128
    )

    Arguments

    data

    Object containing consecutive data points (timesteps). The data should be 2D, and axis 1 is expected to be the time dimension.

    targets

    Targets corresponding to timesteps in data. It should have same length as data.

    length

    Length of the output sequences (in number of timesteps).

    sampling_rate

    Period between successive individual timesteps within sequences. For rate r, timesteps data[i], data[i-r], ... data[i - length] are used for create a sample sequence.

    stride

    Period between successive output sequences. For stride s, consecutive output samples would be centered around data[i], data[i+s], data[i+2*s], etc.

    start_index, end_index

    Data points earlier than start_index or later than end_index will not be used in the output sequences. This is useful to reserve part of the data for test or validation.

    shuffle

    Whether to shuffle output samples, or instead draw them in chronological order.

    reverse

    Boolean: if true, timesteps in each output sample will be in reverse chronological order.

    batch_size

    Number of timeseries samples in each batch (except maybe the last one).

    Value

    An object that can be passed to generator based training functions (e.g. fit_generator()).ma