Time Series Generator module

class time_series_generator.time_series_generator.TimeseriesGenerator(data, targets, length, sampling_rate=1, length_output=1, sampling_rate_output=1, stride=1, start_index=0, end_index=None, shuffle=False, reverse=False, batch_size=9223372036854775807, augmentation=0, overlap=0)

Bases: object

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](/utils/#sequence) instance.

# Examples

```python 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]]))

```

get_config()

Returns the TimeseriesGenerator configuration as Python dictionary.

# Returns

A Python dictionary with the TimeseriesGenerator configuration.

to_json(**kwargs)

Returns a JSON string containing the timeseries generator configuration. To load a generator from a JSON string, use keras.preprocessing.sequence.timeseries_generator_from_json(json_string).

# Arguments

**kwargs: Additional keyword arguments

to be passed to json.dumps().

# Returns

A JSON string containing the tokenizer configuration.

time_series_generator.time_series_generator.timeseries_generator_from_json(json_string)

Parses a JSON timeseries generator configuration file and returns a timeseries generator instance.

# Arguments

json_string: JSON string encoding a timeseries

generator configuration.

# Returns

A Keras TimeseriesGenerator instance