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 corresponding to timesteps in data. It should have same length as data. Length of the output sequences (in number of timesteps). 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. 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. 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. Whether to shuffle output samples, or instead draw them in chronological order. Boolean: if true, timesteps in each output sample will be in reverse chronological order. 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