Hi folks! Thanks for this fantastic contribution. I'm excited to test the capabilities of this package.

I have a hard time to extract features constructed by tsfel for a univariate time series rolled by date. For example, I have a pandas dataframe with m dates and n features, and I want to estimate the tsfel feature set given fixed window size. As a result, I should get a dataframe of shape m dates and n times y (number of variables derived from tsfel). Any comments are welcome.

Thanks in advance!

Dear authors, First of all, congratulations on this great project very helpful fo all the community. I have a issue related to the number of extracted features samples: I execute this call X = ts.time_series_features_extractor(cfg, tmp_data, fs = 32, window_size=32, overlap=0, verbose = 0)

On my accelerometer data frame of dimension 160 x 3, 160 samples and three columns ['X','Y','Z']. From this call, X has a dimension of 1 x 789. It returns a single sample of features for all the 160 x 3 accelerometer samples. However, this does not seem right. Since window _size = 32 (1 second of time frame), it has to return to me an X whit dimension 5 x 789. How is this possible.

Hi everyone,

I'm looking to extract features from 3 IMUs each containing a 3-axis Accelerometer and Gyroscopes. I have created a dataframe to combine the data from all of them 3 IMUs x 2 sensors (Acc, Gry) x 3 axis (xyz) = 18 columns + Timestamp

I started out calling the tsfel.get_features_by_domain on the entire dataframe, but that never progressed from 0% Complete. Then, I much reduce the problem:

• features.json: I copied the original file in the older 'feature_extraction' changed the 'use' from 'yes' to 'no's so it will only calculate [Min, Max, Mean, Median]
• data = selected only one columns and 100 rows with float numbers

` cfg = tsfel.get_features_by_domain(domain = 'statistical', json_path = 'features.json')

data = df.loc[s_times[0]:f_times[0], 'Neck.Acc.X'][:101].to_list()

df_tsfel = tsfel.time_series_features_extractor( # configuration file with features to be extracted dict_features = cfg,
# dataframe window to calculate features window on signal_windows = data,
# sampling frequency of original signal fs = 100, # sliding window size window_size = 100 ) `

It surely can't get any simpler than this and still it doesn't leave the 0%

There must be something wrong with the way I set up stuff. Can someone help please? I'm currentely writting a paper and will need to give up using this package if I dont' manage to sort this out...

Some extra info:

• Using tsfel==0.1.4
• Windows 10
• Sample data attached. ** sample.zip **

Hello! thank you for the tsfel package.

Do you plan to push recently made updates to the PyPI? I see that there have been a number of changes to tsfel since 14th Feb.

If not, would you recommend that we use the development branch or stick with v0.1.4

The X_train data reduced from 208 rows to just 1 row, resulting error for further execution of the code. What can go wrong?

Here is the code:

X_train, X_test, y_train, y_test = train_test_split(X, y, test_size=0.33, random_state=42)
print(X_train)

cfg = tsfel.get_features_by_domain()
# Get features
X_train = tsfel.time_series_features_extractor(cfg, X_train, fs=fs)
X_test = tsfel.time_series_features_extractor(cfg, X_test, fs=fs)
print(X_train)
print(X_test)

corr_features = tsfel.correlated_features(X_train)
X_train.drop(corr_features, axis=1, inplace=True)
X_test.drop(corr_features, axis=1, inplace=True)

I'm using TSFEL on a Windows 10 machine and end up with the following error message whenever I enable the progress bar for feature extraction:

UnicodeEncodeError: 'charmap' codec can't encode character '\u2588' in position 12: character maps to <undefined>

I'm not really sure why this problem occurs but this Stackoverflow thread provides some suggestions on how to mitigate the issue.

If I set the verbose parameter to 0, everything works as expected.

Dear all,

Playing with your tool I wanted to obtain the spectral features for a given signal

cfg = tsfel.get_features_by_domain(domain='spectral')
len(cfg['spectral'].keys())
26

26 spectral features, nice!

But, when I calculated those features

#Fs previously obtained from data
X = tsfel.time_series_features_extractor(cfg, data,fs=Fs)
X.size
335

335 elements!.

I would love to iterate among several time-series, and obtain a feature matrix. I would like to know a priori the size of the X features, as I may create a specific variable array to store the values. I know that certain features of the signal are computed in time slots (such as FFT_mean_coeff), but is really time consuming to annotate how many results per feature I should expect.

Thus, is there any option to know a priori how many elements will be in the X series?

Dear all, Thank you for your incredible work.

I am giving a try with your software, analyzing some neural recordings that I have. Because of computational power, I am running my code through Google's Colaboratory. However, since this tool has a fixed time for running, it should really help a verbose option when running

tsfel.time_series_features_extractor(cfg,data)

as it may help to calculate the amount of time that certain feature calculation should take

Hi, thanks for this tool! It's a huge help. I'm struggling with how I go about extracting some but not all features from the spectral domain.

To extract all we use something like this: cfg_file = tsfel.get_features_by_domain('spectral') data = tsfel.time_series_features_extractor(cfg_file, data, fs=fs)

Which function can we use to extract a list of chosen features? Thanks!

Hi,

I am using tsfel for 564 timeseries analysis, I want to extract the features of each time course and get a dataframe containing the features for all time courses (they should have the same column and each row represents a specific time course)

So I used a loop for this, my dataset has nan in some time course.

My code looks like this, but it only shows the feature extraction started, never finished and could not return the features. Any suggestions on this?

Many thanks!

Hi,

I am wondering if there is a gui version of tsfel. Also, what should the input data look like? Is this able to compute multiple timeseries at the same time and extract their features and cluster them based on the features?

• The module will generate a different number of features depending on the length of the time series. But I got the different lengths of time series, how to make them all have the same number of the feature so I can merge them?
• Does it have some group by id feature? so I can merge all the ts data into one file and process them together?
• I saw in the example notebook, we can share a google link to [email protected] then we can extract features from that shared file name, but it gives an error.

Hi,

I have installed tsfel in an empty virtual environment inside Dockerfile:

# install python and packages
RUN apt-get -y install python3 python3-venv python3-pip
RUN python3 -m venv /home/rstudio/venv
RUN /home/rstudio/venv/bin/pip3 install --upgrade pip setuptools wheel
RUN /home/rstudio/venv/bin/pip3 install tsfel

When I want to extract all features, I get an error:

! AttributeError: module 'scipy.stats' has no attribute 'median_absolute_deviation'

When I extract the MFCC and LPCC feature matrix, how can I tell that there is no problem with my extraction? Is it possible to reverse the reconstruction to calculate their relative errors?

First of all, great package - many thanks for your contributions!

One request is for the ability to specify a "batch" identifier column within a data frame and group by that column value as well as persisting those column values on the output feature matrix (similar to tsfresh which allows for an optional column_id parameter to be provided by the user).

As an example, after loading the UCIHAR dataset to a flattened/stacked dataframe:

batchID time x_acc y_acc z_acc 000001 1 X Y Z 000001 2 X Y Z 000001 3 X Y Z ... 000001 128 X Y Z
000002 1 X Y Z
... 000003 1 X Y Z
...

Currently what is returned from the following is a dataframe without any reference to the input grouping:

import tsfel
cfg_file = tsfel.get_features_by_domain()
df_train_tsfel = df_train.set_index(['batchid', 'time'])
headers = df_train_tsfel.columns
tsfel_features = tsfel.time_series_features_extractor(cfg_file, df_train_tsfel, fs=50, window_size=250, header_names=headers)

Alternatively, could also just implicitly pass and utilize a pre-existing df index, if present. Apologies if this is already possible and I just missed it within the documentation. Ultimately the desired output would look like the following:

batchID x_acc_feature1 x_acc_feature2 ... z_acc_featureN 000001 ... 000002 ... 000003 ... ...

Replace pd.append with pd.concat.

pd.append() deprecated since version 1.4.0: Use concat() instead. For further details see Deprecated DataFrame.append and Series.append