`timeseries_dataset_from_array`

function```
tf.keras.preprocessing.timeseries_dataset_from_array(
data,
targets,
sequence_length,
sequence_stride=1,
sampling_rate=1,
batch_size=128,
shuffle=False,
seed=None,
start_index=None,
end_index=None,
)
```

Creates a dataset of sliding windows over a timeseries provided as array.

This function takes in a sequence of data-points gathered at equal intervals, along with time series parameters such as length of the sequences/windows, spacing between two sequence/windows, etc., to produce batches of timeseries inputs and targets.

**Arguments**

**data**: Numpy array or eager tensor containing consecutive data points (timesteps). Axis 0 is expected to be the time dimension.**targets**: Targets corresponding to timesteps in`data`

. It should have same length as`data`

.`targets[i]`

should be the target corresponding to the window that starts at index`i`

(see example 2 below). Pass None if you don't have target data (in this case the dataset will only yield the input data).**sequence_length**: Length of the output sequences (in number of timesteps).**sequence_stride**: Period between successive output sequences. For stride`s`

, output samples would start at index`data[i]`

,`data[i + s]`

,`data[i + 2 * s]`

, etc.**sampling_rate**: Period between successive individual timesteps within sequences. For rate`r`

, timesteps`data[i], data[i + r], ... data[i + sequence_length]`

are used for create a sample sequence.**batch_size**: Number of timeseries samples in each batch (except maybe the last one).**shuffle**: Whether to shuffle output samples, or instead draw them in chronological order.**seed**: Optional int; random seed for shuffling.**start_index**: Optional int; data points earlier (exclusive) 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**: Optional int; data points later (exclusive) than`end_index`

will not be used in the output sequences. This is useful to reserve part of the data for test or validation.

**Returns**

A tf.data.Dataset instance. If `targets`

was passed, the dataset yields
tuple `(batch_of_sequences, batch_of_targets)`

. If not, the dataset yields
only `batch_of_sequences`

.

Example 1:
Consider indices `[0, 1, ... 99]`

.
With `sequence_length=10, sampling_rate=2, sequence_stride=3`

,
`shuffle=False`

, the dataset will yield batches of sequences
composed of the following indices:

```
First sequence: [0 2 4 6 8 10 12 14 16 18]
Second sequence: [3 5 7 9 11 13 15 17 19 21]
Third sequence: [6 8 10 12 14 16 18 20 22 24]
...
Last sequence: [78 80 82 84 86 88 90 92 94 96]
```

In this case the last 3 data points are discarded since no full sequence can be generated to include them (the next sequence would have started at index 81, and thus its last step would have gone over 99).

Example 2: temporal regression. Consider an array `data`

of scalar
values, of shape `(steps,)`

. To generate a dataset that uses the past 10
timesteps to predict the next timestep, you would use:

```
input_data = data[:-10]
targets = data[10:]
dataset = tf.keras.preprocessing.timeseries_dataset_from_array(
input_data, targets, sequence_length=10)
for batch in dataset:
inputs, targets = batch
assert np.array_equal(inputs[0], data[:10]) # First sequence: steps [0-9]
assert np.array_equal(targets[0], data[10]) # Corresponding target: step 10
break
```

`pad_sequences`

function```
tf.keras.preprocessing.sequence.pad_sequences(
sequences, maxlen=None, dtype="int32", padding="pre", truncating="pre", value=0.0
)
```

Pads sequences to the same length.

This function transforms a list (of length `num_samples`

)
of sequences (lists of integers)
into a 2D Numpy array of shape `(num_samples, num_timesteps)`

.
`num_timesteps`

is either the `maxlen`

argument if provided,
or the length of the longest sequence in the list.

Sequences that are shorter than `num_timesteps`

are padded with `value`

until they are `num_timesteps`

long.

Sequences longer than `num_timesteps`

are truncated
so that they fit the desired length.

The position where padding or truncation happens is determined by
the arguments `padding`

and `truncating`

, respectively.
Pre-padding or removing values from the beginning of the sequence is the
default.

```
``````
>>> sequence = [[1], [2, 3], [4, 5, 6]]
>>> tf.keras.preprocessing.sequence.pad_sequences(sequence)
array([[0, 0, 1],
[0, 2, 3],
[4, 5, 6]], dtype=int32)
```

```
``````
>>> tf.keras.preprocessing.sequence.pad_sequences(sequence, value=-1)
array([[-1, -1, 1],
[-1, 2, 3],
[ 4, 5, 6]], dtype=int32)
```

```
``````
>>> tf.keras.preprocessing.sequence.pad_sequences(sequence, padding='post')
array([[1, 0, 0],
[2, 3, 0],
[4, 5, 6]], dtype=int32)
```

```
``````
>>> tf.keras.preprocessing.sequence.pad_sequences(sequence, maxlen=2)
array([[0, 1],
[2, 3],
[5, 6]], dtype=int32)
```

**Arguments**

**sequences**: List of sequences (each sequence is a list of integers).**maxlen**: Optional Int, maximum length of all sequences. If not provided, sequences will be padded to the length of the longest individual sequence.**dtype**: (Optional, defaults to int32). Type of the output sequences. To pad sequences with variable length strings, you can use`object`

.**padding**: String, 'pre' or 'post' (optional, defaults to 'pre'): pad either before or after each sequence.**truncating**: String, 'pre' or 'post' (optional, defaults to 'pre'): remove values from sequences larger than`maxlen`

, either at the beginning or at the end of the sequences.**value**: Float or String, padding value. (Optional, defaults to 0.)

**Returns**

Numpy array with shape `(len(sequences), maxlen)`

**Raises**

**ValueError**: In case of invalid values for`truncating`

or`padding`

, or in case of invalid shape for a`sequences`

entry.

`TimeseriesGenerator`

class```
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,
)
```

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**: 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 instance.

**Example**

s

```
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]]))
```