|---| | [ View source on GitHub](https://github.com/keras-team/keras/tree/v2.15.0/keras/preprocessing/sequence.py#L55-L245) | Utility class for generating batches of temporal data. Inherits From: [`Sequence`](https://www.tensorflow.org/versions/r2.15/api_docs/python/tf/keras/utils/Sequence) tf.keras.preprocessing.sequence.TimeseriesGenerator( data, targets, length, sampling_rate=1, stride=1, start_index=0, end_index=None, shuffle=False, reverse=False, batch_size=128 ) > [!WARNING] > **Deprecated:** [`tf.keras.preprocessing.sequence.TimeseriesGenerator`](https://www.tensorflow.org/versions/r2.15/api_docs/python/tf/keras/preprocessing/sequence/TimeseriesGenerator) does not operate on tensors and is not recommended for new code. Prefer using a [`tf.data.Dataset`](https://www.tensorflow.org/versions/r2.15/api_docs/python/tf/data/Dataset) which provides a more efficient and flexible mechanism for batching, shuffling, and windowing input. See the [tf.data guide](https://www.tensorflow.org/guide/data) for more details. 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` | Data points earlier than `start_index` will not be used in the output sequences. This is useful to reserve part of the data for test or validation. | | `end_index` | Data points 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](https://www.tensorflow.org/api_docs/python/tf/keras/utils/Sequence) instance. ||



| ## Examples || |---|---| |
from keras.src.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]]))
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## Methods ### `get_config` [View source](https://github.com/keras-team/keras/tree/v2.15.0/keras/preprocessing/sequence.py#L195-L228) get_config() Returns the TimeseriesGenerator configuration as Python dictionary.


| Returns || |---|---| | A Python dictionary with the TimeseriesGenerator configuration. ||
### `on_epoch_end` [View source](https://github.com/keras-team/keras/tree/v2.15.0/keras/utils/data_utils.py#L561-L563) on_epoch_end() Method called at the end of every epoch. ### `to_json` [View source](https://github.com/keras-team/keras/tree/v2.15.0/keras/preprocessing/sequence.py#L230-L245) to_json( **kwargs ) Returns a JSON string containing the generator's configuration.


| Args || |---|---| | `**kwargs` | Additional keyword arguments to be passed to `json.dumps()`. |



| Returns || |---|---| | A JSON string containing the tokenizer configuration. ||
### `__getitem__` [View source](https://github.com/keras-team/keras/tree/v2.15.0/keras/preprocessing/sequence.py#L170-L193) __getitem__( index ) Gets batch at position `index`.


| Args || |---|---| | `index` | position of the batch in the Sequence. |



| Returns || |---|---| | A batch ||
### `__iter__` [View source](https://github.com/keras-team/keras/tree/v2.15.0/keras/utils/data_utils.py#L565-L568) __iter__() Create a generator that iterate over the Sequence. ### `__len__` [View source](https://github.com/keras-team/keras/tree/v2.15.0/keras/preprocessing/sequence.py#L165-L168) __len__() Number of batch in the Sequence.


| Returns || |---|---| | The number of batches in the Sequence. ||