Package: tensorflow-time-series-dataset
TensorFlow time-series DatasetAbout The ProjectInstallationUsageExample DataSingle-Step PredictionMulti-Step PredictionPreprocessing: Add Metadata featuresContributingLicenseContactAcknowledgmentsAbout The ProjectThis python package should help you to create TensorFlow datasets for time-series data.InstallationThis package is available onPyPI.
You install it and all of its dependencies using pip:pip install tensorflow_time_series_datasetUsageExample DataSuppose you have a dataset in the following form:import numpy as np
import pandas as pd

# make things determeinisteic
np.random.seed(1)

columns=['x1', 'x2', 'x3']
periods=48 * 14
test_df=pd.DataFrame(
    index=pd.date_range(
        start='1/1/1992',
        periods=periods,
        freq='30min'
    ),
    data=np.stack(
        [
            np.random.normal(0,0.5,periods),
            np.random.normal(1,0.5,periods),
            np.random.normal(2,0.5,periods)
        ],
        axis=1
    ),
    columns=columns
)
test_df.head()

                           x1        x2        x3
1992-01-01 00:00:00  0.812173  1.205133  1.578044
1992-01-01 00:30:00 -0.305878  1.429935  1.413295
1992-01-01 01:00:00 -0.264086  0.550658  1.602187
1992-01-01 01:30:00 -0.536484  1.159828  1.644974
1992-01-01 02:00:00  0.432704  1.159077  2.005718Single-Step PredictionThe factory classWindowedTimeSeriesDatasetFactoryis used to create a TensorFlow dataset from pandas dataframes, or other data sources as we will see later.
We will use it now to create a dataset with48historic time-steps as the input to predict a single time-step in the future.from tensorflow_time_series_dataset.factory import WindowedTimeSeriesDatasetFactory as Factory

factory_kwargs=dict(
    history_size=48,
    prediction_size=1,
    history_columns=['x1', 'x2', 'x3'],
    prediction_columns=['x3'],
    batch_size=4,
    drop_remainder=True,
)
factory=Factory(**factory_kwargs)
ds1=factory(test_df)
ds1This returns the following TensorFlow Dataset:<_PrefetchDataset element_spec=(TensorSpec(shape=(4, 48, 3), dtype=tf.float32, name=None), TensorSpec(shape=(4, 1, 1), dtype=tf.float32, name=None))>We can plot the result with the utility functionplot_path:from tensorflow_time_series_dataset.utils.visualisation import plot_patch

githubusercontent="https://raw.githubusercontent.com/MArpogaus/tensorflow_time_series_dataset/master/"

fig=plot_patch(
    ds1,
    figsize=(8,4),
    **factory_kwargs
)

fname='.images/example1.svg'
fig.savefig(fname)

f"[[{githubusercontent}{fname}]]"Multi-Step PredictionLets now increase the prediction size to6half-hour time-steps.factory_kwargs.update(dict(
    prediction_size=6
))
factory=Factory(**factory_kwargs)
ds2=factory(test_df)
ds2This returns the following TensorFlow Dataset:<_PrefetchDataset element_spec=(TensorSpec(shape=(4, 48, 3), dtype=tf.float32, name=None), TensorSpec(shape=(4, 6, 1), dtype=tf.float32, name=None))>Again, lets plot the results to see what changed:fig=plot_patch(
    ds2,
    figsize=(8,4),
    **factory_kwargs
)

fname='.images/example2.svg'
fig.savefig(fname)

f"[[{githubusercontent}{fname}]]"Preprocessing: Add Metadata featuresPreprocessors can be used to transform the data before it is fed into the model.
A Preprocessor can be any python callable.
In this case we will be using the a class calledCyclicalFeatureEncoderto encode our one-dimensional cyclical features like thetimeorweekdayto two-dimensional coordinates using a sine and cosine transformation as suggested inthis blogpost.import itertools
from tensorflow_time_series_dataset.preprocessors import CyclicalFeatureEncoder
encs = {
    "weekday": dict(cycl_max=6),
    "dayofyear": dict(cycl_max=366, cycl_min=1),
    "month": dict(cycl_max=12, cycl_min=1),
    "time": dict(
        cycl_max=24 * 60 - 1,
        cycl_getter=lambda df, k: df.index.hour * 60 + df.index.minute,
    ),
}
factory_kwargs.update(dict(
    meta_columns=list(itertools.chain(*[[c+'_sin', c+'_cos'] for c in encs.keys()]))
))
factory=Factory(**factory_kwargs)
for name, kwargs in encs.items():
    factory.add_preprocessor(CyclicalFeatureEncoder(name, **kwargs))

ds3=factory(test_df)
ds3This returns the following TensorFlow Dataset:<_PrefetchDataset element_spec=((TensorSpec(shape=(4, 48, 3), dtype=tf.float32, name=None), TensorSpec(shape=(4, 1, 8), dtype=tf.float32, name=None)), TensorSpec(shape=(4, 6, 1), dtype=tf.float32, name=None))>Again, lets plot the results to see what changed:fig=plot_patch(
    ds3,
    figsize=(8,4),
    **factory_kwargs
)

fname='.images/example3.svg'
fig.savefig(fname)

f"[[{githubusercontent}{fname}]]"ContributingAny Contributions are greatly appreciated! If you have a question, an issue or would like to contribute, please read ourcontributing guidelines.LicenseDistributed under theApache License 2.0ContactMarcel Arpogaus-marcel.arpogaus@gmail.comProject Link:https://github.com/MArpogaus/tensorflow_time_series_datasetAcknowledgmentsParts of this work have been funded by the Federal Ministry for the Environment, Nature Conservation and Nuclear Safety due to a decision of the German Federal Parliament (AI4Grids: 67KI2012A).