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https://github.com/marpogaus/tensorflow_time_series_dataset

A Tensorflow Dataset Factory for time-series data.
https://github.com/marpogaus/tensorflow_time_series_dataset

machine-learning tensorflow2 time-series

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A Tensorflow Dataset Factory for time-series data.

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# TensorFlow time-series Dataset

1. [About The Project](#orgd7601fa)
2. [Installation](#org4baeaee)
3. [Usage](#org78d3afa)
1. [Example Data](#org437e43f)
2. [Single-Step Prediction](#org87ebbfe)
3. [Multi-Step Prediction](#org11d5abf)
4. [Preprocessing: Add Metadata features](#org3006a48)
4. [Contributing](#org5b01553)
5. [License](#org8585d54)
6. [Contact](#org66ff6ba)
7. [Acknowledgments](#org8768712)

## About The Project

This python package should help you to create TensorFlow datasets for time-series data.

## Installation

This package is available on [PyPI](https://pypi.org/project/tensorflow-time-series-dataset/).
You install it and all of its dependencies using pip:

pip install tensorflow_time_series_dataset

## Usage

### Example Data

Suppose 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 2.005718

### Single-Step Prediction

The factory class `WindowedTimeSeriesDatasetFactory` is 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 with `48` historic 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)
ds1

This 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 function `plot_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}]]"

![img](https://raw.githubusercontent.com/MArpogaus/tensorflow_time_series_dataset/master/.images/example1.svg)

### Multi-Step Prediction

Lets now increase the prediction size to `6` half-hour time-steps.

factory_kwargs.update(dict(
prediction_size=6
))
factory=Factory(**factory_kwargs)
ds2=factory(test_df)
ds2

This 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}]]"

![img](https://raw.githubusercontent.com/MArpogaus/tensorflow_time_series_dataset/master/.images/example2.svg)

### Preprocessing: Add Metadata features

Preprocessors 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 called `CyclicalFeatureEncoder` to encode our one-dimensional cyclical features like the *time* or *weekday* to two-dimensional coordinates using a sine and cosine transformation as suggested in [this 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)
ds3

This 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}]]"

![img](https://raw.githubusercontent.com/MArpogaus/tensorflow_time_series_dataset/master/.images/example3.svg)

## Contributing

Any Contributions are greatly appreciated! If you have a question, an issue or would like to contribute, please read our [contributing guidelines](CONTRIBUTING.md).

## License

Distributed under the [Apache License 2.0](LICENSE)

## Contact

[Marcel Arpogaus](https://github.com/marpogaus) - [[email protected]](mailto:[email protected])

Project Link:

## Acknowledgments

Parts 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).