A toolkit for flexible time-series processing & feature extraction

tsflex

tsflex is a toolkit for flexible time-series processing & feature extraction, making few assumptions about input data.

Installation

If you are using pip, just execute the following command:

pip install tsflex

Why tsflex? 

• flexible;
  • handles multi-variate time-series
  • versatile function support
    => integrates natively with many packages for processing (e.g., scipy.signal) & feature extraction (e.g., numpy, scipy.stats)
  • feature-extraction handles multiple strides & window sizes
• efficient view-based operations
  => extremely low memory peak & fast execution times (see benchmarks)
• maintains the time-index of the data
• makes little to no assumptions about the time-series data

Usage

tsflex is built to be intuitive, so we encourage you to copy-paste this code and toy with some parameters!

Series processing

import pandas as pd; import scipy.signal as ssig; import numpy as np
from tsflex.processing import SeriesProcessor, SeriesPipeline

# 1. -------- Get your time-indexed data --------
# Data contains 3 columns; ["ACC_x", "ACC_y", "ACC_z"]
url = "https://github.com/predict-idlab/tsflex/raw/main/examples/data/empatica/acc.parquet"
data = pd.read_parquet(url).set_index("timestamp")

# 2 -------- Construct your processing pipeline --------
processing_pipe = SeriesPipeline(
    processors=[
        SeriesProcessor(function=np.abs, series_names=["ACC_x", "ACC_y", "ACC_z"]),
        SeriesProcessor(ssig.medfilt, ["ACC_x", "ACC_y", "ACC_z"], kernel_size=5)  # (with kwargs!)
    ]
)
# -- 2.1. Append processing steps to your processing pipeline
processing_pipe.append(SeriesProcessor(ssig.detrend, ["ACC_x", "ACC_y", "ACC_z"]))

# 3 -------- Process the data --------
processing_pipe.process(data=data)

Feature extraction

import pandas as pd; import scipy.stats as ssig; import numpy as np
from tsflex.features import FeatureDescriptor, FeatureCollection, NumpyFuncWrapper

# 1. -------- Get your time-indexed data --------
# Data contains 1 column; ["TMP"]
url = "https://github.com/predict-idlab/tsflex/raw/main/examples/data/empatica/tmp.parquet"
data = pd.read_parquet(url).set_index("timestamp")

# 2 -------- Construct your feature collection --------
fc = FeatureCollection(
    feature_descriptors=[
        FeatureDescriptor(
            function=NumpyFuncWrapper(func=ssig.skew, output_names="skew"),
            series_name="TMP", 
            window="5min",  # Use 5 minutes 
            stride="2.5min",  # With steps of 2.5 minutes
        )
    ]
)
# -- 2.1. Add features to your feature collection
fc.add(FeatureDescriptor(np.min, "TMP", '2.5min', '2.5min'))

# 3 -------- Calculate features --------
fc.calculate(data=data)

GitHub

https://github.com/predict-idlab/tsflex