Streaming Feature Extraction#

This notebook demonstrates how to use the interpreTS library for feature extraction in a streaming context. We’ll process time series data as it arrives in real-time.

Step 1: Import Libraries#

[ ]:

from interpreTS.core.feature_extractor import FeatureExtractor, Features

import random
import time
from datetime import datetime, timedelta

Step 2: Define a Data Stream Generator#

We’ll simulate a time series data stream where new data points are generated every 30 seconds.

[2]:

def generate_stream():
    current_time = datetime.now()
    for i in range(200):    # Generate 200 data points
        yield {
            'id': 'series_1',   # Identifier for the time series
            'time': current_time + timedelta(seconds=30 * i),   # Timestamp
            'value': random.random()    # Randomly generated value
        }
        time.sleep(0.01)

Step 3: Initialize the FeatureExtractor for Streaming#

We’ll set up the FeatureExtractor to calculate the mean and variance from the value column using a window size of 5 data points. The id_column specifies the unique identifier for the time series.

[3]:

feature_extractor_stream = FeatureExtractor(
    features=[Features.MEAN, Features.VARIANCE],    # Features to extract
    feature_column="value", # Data column from which to extract features
    window_size=5,  # Number of points per window
    id_column="id"  # Group by the 'id' column
)

Step 4: Extract Features in a Streaming Context#

Using the extract_features_stream method, we’ll process the data stream and print the extracted features for each window of 5 data points.

[4]:

for features in feature_extractor_stream.extract_features_stream(generate_stream()):
    print(features)

{'mean_value': 0.5302559014050979, 'variance_value': 0.05253585541039717, 'id': 'series_1'}
{'mean_value': 0.5763003255145184, 'variance_value': 0.056443627047807934, 'id': 'series_1'}
{'mean_value': 0.5002972150764308, 'variance_value': 0.0263593570015553, 'id': 'series_1'}
{'mean_value': 0.5603844489807113, 'variance_value': 0.012360516245759023, 'id': 'series_1'}
{'mean_value': 0.5715475273281434, 'variance_value': 0.008600746357379363, 'id': 'series_1'}
{'mean_value': 0.5941913312223666, 'variance_value': 0.01244589462280257, 'id': 'series_1'}
{'mean_value': 0.5599190670382057, 'variance_value': 0.009149717681769174, 'id': 'series_1'}
{'mean_value': 0.6225206242724437, 'variance_value': 0.02310858422510515, 'id': 'series_1'}
{'mean_value': 0.674171340523812, 'variance_value': 0.02774186833184048, 'id': 'series_1'}
{'mean_value': 0.7055771874159391, 'variance_value': 0.016065929277289052, 'id': 'series_1'}
{'mean_value': 0.6704200527203796, 'variance_value': 0.019687636663485837, 'id': 'series_1'}
{'mean_value': 0.7370312361537542, 'variance_value': 0.019989380605827743, 'id': 'series_1'}
{'mean_value': 0.7614573392521315, 'variance_value': 0.028344792272259202, 'id': 'series_1'}
{'mean_value': 0.7462062104469848, 'variance_value': 0.027418583408698387, 'id': 'series_1'}
{'mean_value': 0.683236658692616, 'variance_value': 0.0638961414415679, 'id': 'series_1'}
{'mean_value': 0.7533965696049488, 'variance_value': 0.06469337066438814, 'id': 'series_1'}
{'mean_value': 0.6009304314789746, 'variance_value': 0.12135292176383225, 'id': 'series_1'}
{'mean_value': 0.45481097398317605, 'variance_value': 0.09558484935286735, 'id': 'series_1'}
{'mean_value': 0.49383207269379853, 'variance_value': 0.12440586660297606, 'id': 'series_1'}
{'mean_value': 0.6402472662930938, 'variance_value': 0.14392343772065924, 'id': 'series_1'}
{'mean_value': 0.500639736837788, 'variance_value': 0.14952572549053306, 'id': 'series_1'}
{'mean_value': 0.6395972139230727, 'variance_value': 0.11825003650162931, 'id': 'series_1'}
{'mean_value': 0.7020031645584106, 'variance_value': 0.08529487994254997, 'id': 'series_1'}
{'mean_value': 0.5351198405420222, 'variance_value': 0.11686757276580009, 'id': 'series_1'}
{'mean_value': 0.34296792903953705, 'variance_value': 0.0860058836031357, 'id': 'series_1'}
{'mean_value': 0.4551534197304643, 'variance_value': 0.10457556910624252, 'id': 'series_1'}
{'mean_value': 0.3273042730208836, 'variance_value': 0.08050833819288028, 'id': 'series_1'}
{'mean_value': 0.3309938742453727, 'variance_value': 0.08230038798771211, 'id': 'series_1'}
{'mean_value': 0.44697101901178493, 'variance_value': 0.08409072663381771, 'id': 'series_1'}
{'mean_value': 0.5967782821074181, 'variance_value': 0.051616424145450646, 'id': 'series_1'}
{'mean_value': 0.6374646332557311, 'variance_value': 0.07170589046907584, 'id': 'series_1'}
{'mean_value': 0.703534238312746, 'variance_value': 0.02683195723941485, 'id': 'series_1'}
{'mean_value': 0.7458944204102318, 'variance_value': 0.023647371512954124, 'id': 'series_1'}
{'mean_value': 0.6526149675845637, 'variance_value': 0.06950532653203104, 'id': 'series_1'}
{'mean_value': 0.6232617713365499, 'variance_value': 0.06500345585389512, 'id': 'series_1'}
{'mean_value': 0.5060678553777406, 'variance_value': 0.03967319439455708, 'id': 'series_1'}
{'mean_value': 0.5501643895715762, 'variance_value': 0.046570136385489694, 'id': 'series_1'}
{'mean_value': 0.444881200053695, 'variance_value': 0.039769971232088724, 'id': 'series_1'}
{'mean_value': 0.468731190991121, 'variance_value': 0.031330190390015965, 'id': 'series_1'}
{'mean_value': 0.4208258800721806, 'variance_value': 0.02398170046491189, 'id': 'series_1'}
{'mean_value': 0.5205838982711584, 'variance_value': 0.055593030297842946, 'id': 'series_1'}
{'mean_value': 0.5742968175596824, 'variance_value': 0.08818626234312965, 'id': 'series_1'}
{'mean_value': 0.6634698689863772, 'variance_value': 0.0651187535947912, 'id': 'series_1'}
{'mean_value': 0.779787223325241, 'variance_value': 0.04387021021506346, 'id': 'series_1'}
{'mean_value': 0.8298824416680489, 'variance_value': 0.016028945320975412, 'id': 'series_1'}
{'mean_value': 0.7828329964264761, 'variance_value': 0.02030492614904039, 'id': 'series_1'}
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