timeseries_generator
Utility function for generating batches of temporal data.
Description
Utility function for generating batches of temporal data.
Usage
timeseries_generator(
data,
targets,
length,
sampling_rate = 1,
stride = 1,
start_index = 0,
end_index = NULL,
shuffle = FALSE,
reverse = FALSE,
batch_size = 128
) Arguments
| Arguments | Description |
|---|---|
| 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