Oh no, you encountered a problem while using *tsfesh*.

We, the maintainers, are happy to help you. When opening an issue, please provide the following information to us:

1. Your operating system

2. The version of *tsfresh* that you are using

3. The data on which the problem occurred (please do not upload 1000s of time series but try to boil the problem down to a small group or even a singular one)

4. A minimal code snippet which reproduces the problem/bug

For questions, you can also use our [gitter chatroom](https://gitter.im/tsfresh/)

Version 0.7.0

- new rolling utility to use tsfresh for time series forecasting tasks

- bugfixes

- index_mass_quantile was using global index of time series container

- an index with same name as id_column was breaking parallelization

- friedrich_coefficients and max_langevin_fixed_point were occasionally stalling

- fixed bug in the RelevantFeatureAugmenter regarding the evaluate_only_added_features parameters

1. **Does tsfresh support different time series lengths?**

2. **Is it possible to extract features from rolling/shifted time series?**

Yes, the :func:`tsfresh.dataframe_functions.roll_time_series` function allows to conviniently create a rolled

time series datframe from your data. You just have to transform your data into one of the supported tsfresh

:ref:`data-formats-label`.

Then, the :func:`tsfresh.dataframe_functions.roll_time_series` give you a DataFrame with the rolled time series,

that you can pass to tsfresh.

On the following page you can find a detailed description: :ref:`rolling-label`.

Lets assume that we have a DataFrame of one of the tsfresh :ref:`data-formats-label`.

The "sort" column of such a container gives a sequential state to the individual measurements.

In the case of time series this can be the *time* dimension while in the case of spectra the order is given by the

*wavelength* or *frequency* dimensions.

We can exploit this sequence to generate more input data out of single time series, by *rolling* over the data.

Imagine the following situation:

You have the data of certain sensors (e.g. EEG measurements) as the base to classify patients into a healthy and not

healthy group (we oversimplify the problem here).

Lets say you have sensor data of 100 time steps, so you may extract features for the forecasting of the patients

healthiness by a classification algorithm.

If you also have measurements of the healthiness for those 100 time steps (this is the target vector), then you could

predict the healthiness of the patient in every time step, which essentially states a time series forecasting problem.

So, to do that, you want to extract features in every time step of the original time series while for example looking at

the last 10 steps.

A rolling mechanism creates such time series for every time step by creating sub time series of the sensor data of the

last 10 time steps.

Another example can be found in streaming data, e.g. in Industry 4.0 applications.

Here you typically get one new data row at a time and use this to for example predict machine failures. To train your model,

on all temporal coherent subsets of data, which is the process of *rolling*. In tsfresh, this is implemented in the

function :func:`tsfresh.utilities.dataframe_functions.roll_time_series`.

To see what this does in real-world applications, we look into the following example flat DataFrame in tsfresh format

where you have measured the values from two sensors x and y for two different entities (id 1 and 2) in 4 or 2 time

steps (t1 to t9).

Now, we can use :func:`tsfresh.utilities.dataframe_functions.roll_time_series` to get consecutive sub-time series.

E.g. if you set `rolling` to 0, the feature extraction works on the original time series without any rolling.

So it extracts 2 set of features,

We only gave an example for the flat DataFrame format, but rolling actually works on all 3 :ref:`data-formats-label`

that are supported by tsfresh.

matplotlib==2.0.0

seaborn==0.7.1

ipython==5.3.0

notebook==4.4.1