# tf.keras.preprocessing.sequence.TimeseriesGenerator

## Class TimeseriesGenerator

Inherits From: Sequence

Utility class for generating batches of temporal data.

This class takes in a sequence of data-points gathered at equal intervals, along with time series parameters such as stride, length of history, etc., to produce batches for training/validation.

#### Arguments:

• data: Indexable generator (such as list or Numpy array) containing consecutive data points (timesteps). The data should be at 2D, and axis 0 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).

#### Returns:

A [Sequence](/utils/#sequence) instance.


Examples:

from keras.preprocessing.sequence import TimeseriesGenerator
import numpy as np

data = np.array([[i] for i in range(50)])
targets = np.array([[i] for i in range(50)])

data_gen = TimeseriesGenerator(data, targets,
length=10, sampling_rate=2,
batch_size=2)
assert len(data_gen) == 20

batch_0 = data_gen[0]
x, y = batch_0
assert np.array_equal(x,
np.array([[[0], [2], [4], [6], [8]],
[[1], [3], [5], [7], [9]]]))
assert np.array_equal(y,
np.array([[10], [11]]))


## Methods

### __init__

__init__(
data,
targets,
length,
sampling_rate=1,
stride=1,
start_index=0,
end_index=None,
shuffle=False,
reverse=False,
batch_size=128
)


### __getitem__

__getitem__(index)


### __iter__

__iter__()


Creates an infinite generator that iterate over the Sequence.

Sequence items.

### __len__

__len__()


### on_epoch_end

on_epoch_end()


Method called at the end of every epoch.