tsfeatures 0.4.5
Calculates various features from time series data.
Requires Python: >=3.7

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# tsfeatures

Calculates various features from time series data. Python implementation of the R package _[tsfeatures](https://github.com/robjhyndman/tsfeatures)_.

# Installation

You can install the *released* version of `tsfeatures` from the [Python package index](pypi.org) with:

``` python
pip install tsfeatures
```

# Usage

The `tsfeatures` main function calculates by default the features used by Montero-Manso, Talagala, Hyndman and Athanasopoulos in [their implementation of the FFORMA model](https://htmlpreview.github.io/?https://github.com/robjhyndman/M4metalearning/blob/master/docs/M4_methodology.html#features).

```python
from tsfeatures import tsfeatures
```

This function receives a panel pandas df with columns `unique_id`, `ds`, `y` and optionally the frequency of the data.

<img src=https://raw.githubusercontent.com/FedericoGarza/tsfeatures/master/.github/images/y_train.png width="152">

```python
tsfeatures(panel, freq=7)
```

By default (`freq=None`) the function will try to infer the frequency of each time series (using `infer_freq` from `pandas` on the `ds` column) and assign a seasonal period according to the built-in dictionary `FREQS`:

```python
FREQS = {'H': 24, 'D': 1,
         'M': 12, 'Q': 4,
         'W':1, 'Y': 1}
```

You can use your own dictionary using the `dict_freqs` argument:

```python
tsfeatures(panel, dict_freqs={'D': 7, 'W': 52})
```

## List of available features

| Features |||
|:--------|:------|:-------------|
|acf_features|heterogeneity|series_length|
|arch_stat|holt_parameters|sparsity|
|count_entropy|hurst|stability|
|crossing_points|hw_parameters|stl_features|
|entropy|intervals|unitroot_kpss|
|flat_spots|lumpiness|unitroot_pp|
|frequency|nonlinearity||
|guerrero|pacf_features||

See the docs for a description of the features. To use a particular feature included in the package you need to import it:

```python
from tsfeatures import acf_features

tsfeatures(panel, freq=7, features=[acf_features])
```

You can also define your own function and use it together with the included features:

```python
def number_zeros(x, freq):

    number = (x == 0).sum()
    return {'number_zeros': number}

tsfeatures(panel, freq=7, features=[acf_features, number_zeros])
```

`tsfeatures` can handle functions that receives a numpy array `x` and a frequency `freq` (this parameter is needed even if you don't use it) and returns a dictionary with the feature name as a key and its value.

## R implementation

You can use this package to call `tsfeatures` from R inside python (you need to have installed R, the packages `forecast` and `tsfeatures`; also the python package `rpy2`):

```python
from tsfeatures.tsfeatures_r import tsfeatures_r

tsfeatures_r(panel, freq=7, features=["acf_features"])
```

Observe that this function receives a list of strings instead of a list of functions.

## Comparison with the R implementation (sum of absolute differences)

### Non-seasonal data (100 Daily M4 time series)

| feature         |   diff | feature         |   diff | feature         |   diff | feature         |   diff |
|:----------------|-------:|:----------------|-------:|:----------------|-------:|:----------------|-------:|
| e_acf10         |   0    | e_acf1         |   0    | diff2_acf1         |   0    | alpha         |   3.2    |
| seasonal_period |   0    | spike         |   0    | diff1_acf10         |   0    | arch_acf         |   3.3    |
| nperiods        |   0    | curvature         |   0    | x_acf1         |   0    | beta         |   4.04    |
| linearity       |   0    | crossing_points         |   0    | nonlinearity         |   0    | garch_r2         |   4.74    |
| hw_gamma        |   0    | lumpiness         |   0    | diff2x_pacf5         |   0    | hurst         |   5.45    |
| hw_beta         |   0    | diff1x_pacf5         |   0    | unitroot_kpss         |   0    | garch_acf         |   5.53    |
| hw_alpha        |   0    | diff1_acf10         |   0    | x_pacf5         |   0    | entropy         |   11.65    |
| trend           |   0    | arch_lm         |   0    | x_acf10         |   0    |
| flat_spots      |   0    | diff1_acf1         |   0    | unitroot_pp         |   0    |
| series_length   |   0    | stability         |   0    | arch_r2         |   1.37    |

To replicate this results use:

``` console
python -m tsfeatures.compare_with_r --results_directory /some/path
                                    --dataset_name Daily --num_obs 100
```

### Sesonal data (100 Hourly M4 time series)

| feature           |   diff | feature      | diff | feature   | diff    | feature    | diff    |
|:------------------|-------:|:-------------|-----:|:----------|--------:|:-----------|--------:|
| series_length     |   0    |seas_acf1     | 0    | trend | 2.28 | hurst | 26.02 |
| flat_spots        |   0    |x_acf1|0| arch_r2 | 2.29 | hw_beta | 32.39 |
| nperiods          |   0    |unitroot_kpss|0| alpha | 2.52 | trough | 35 |
| crossing_points   |   0    |nonlinearity|0| beta | 3.67 | peak | 69 |
| seasonal_period   |   0    |diff1_acf10|0| linearity | 3.97 |
| lumpiness         |   0    |x_acf10|0| curvature | 4.8 |
| stability         |   0    |seas_pacf|0| e_acf10 | 7.05 |
| arch_lm           |   0    |unitroot_pp|0| garch_r2 | 7.32 |
| diff2_acf1        |   0    |spike|0| hw_gamma | 7.32 |
| diff2_acf10       |   0    |seasonal_strength|0.79| hw_alpha | 7.47 |
| diff1_acf1        |   0    |e_acf1|1.67| garch_acf | 7.53 |
| diff2x_pacf5      |   0    |arch_acf|2.18| entropy | 9.45 |

To replicate this results use:

``` console
python -m tsfeatures.compare_with_r --results_directory /some/path \
                                    --dataset_name Hourly --num_obs 100
```

# Authors

* **Federico Garza** - [FedericoGarza](https://github.com/FedericoGarza)
* **Kin Gutierrez** - [kdgutier](https://github.com/kdgutier)
* **Cristian Challu** - [cristianchallu](https://github.com/cristianchallu)
* **Jose Moralez** - [jose-moralez](https://github.com/jose-moralez)
* **Ricardo Olivares** - [rolivaresar](https://github.com/rolivaresar)
* **Max Mergenthaler** - [mergenthaler](https://github.com/mergenthaler)