Tsxtract

High-Performance Time-Series Feature Extraction. Rust Core. Python Ease.

What if time-series feature engineering was 800× faster and used zero defensive memory copies?

Tsxtract is a minimalistic, dependency-light time-series feature extraction library designed to make extracting statistical, temporal, and spectral features across large datasets blazingly fast, memory-efficient, and effortless. It combines a zero-copy Rust engine with a clean, Scikit-Learn-compatible Python interface—ideal for machine learning pipelines, quantitative finance, real-time sensor telemetry, and high-throughput research.

Features • Installation • Quickstart • Benchmarks • Streaming & Sliding Windows • Scikit-Learn Integration • Documentation

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Why Tsxtract?

Traditional Python time-series feature libraries (tsfresh, TSFEL, catch22) force a painful trade-off: wait minutes to hours for feature extraction, or risk Out-Of-Memory (OOM) crashes from defensive copies. Tsxtract eliminates that trade-off.

• ⚡ Blazing Fast: Computes up to 800,256 series/second on standard hardware—outperforming catch22 by 820× and tsfresh by 14,000×.
• 🧠 Zero-Copy Ingestion: Directly borrows contiguous NumPy buffer pointers via PyO3. No data duplication, no DataFrame melting, and zero intermediate memory ballooning.
• 🎯 33 Curated, High-Signal Features: Avoids the curse of dimensionality. Features are mathematically non-redundant ($|r| < 0.70$ for 83.3% of pairs), spanning distribution moments, quantiles, crossings, spectral power, and permutation entropy.
• 🔓 Full Multi-Core Scaling (GIL-Free): Releases Python’s Global Interpreter Lock (GIL) across the entire computation region, saturating all CPU cores with Rayon's work-stealing scheduler.
• 🔄 Real-Time Streaming Ready: Compute streaming features with constant memory $O(1)$ state updates using the built-in StreamingExtractor.

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Key Features

• Zero-Copy Hybrid Architecture: PyO3 bindings pass 2D NumPy pointer references directly into native Rust SIMD and multi-core loops without copying a single byte.
• Batch-First Parallelism: Processes $N$ series in parallel across hardware threads instead of running serial Python loops.
• Dual API Support: Extract raw 2D NumPy matrices for maximum speed, or labeled Pandas/Polars DataFrames for immediate exploratory analysis.
• Scikit-Learn Compatible: Seamlessly drop TsxtractTransformer into any sklearn.pipeline.Pipeline or cross-validation grid search.
• Realfft & Branchless Primitives: Preallocated thread-local FFT workspaces and branchless quantile quickselects ensure predictable sub-millisecond execution.
• Streaming & Sliding Windows: Extract rolling features over continuous data streams without reallocating buffers.

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Installation

Prebuilt Wheels (Recommended)

Precompiled binary wheels are available on PyPI for Linux (x86_64, aarch64), macOS (Apple Silicon arm64, Intel x86_64), and Windows (x64). No Rust compiler required!

# Core install (NumPy only)
pip install tsxtract-rs

# With optional Pandas DataFrame support
pip install "tsxtract-rs[pandas]"

Using uv or conda:

uv add tsxtract-rs

From Source (Development)

git clone https://github.com/Aamod007/Tsxtract.git
cd Tsxtract
pip install maturin
maturin develop --release

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Quickstart

1. Batch Feature Extraction (2D NumPy)

Extract 33 features from 100,000 series in under a second:

import numpy as np
import tsxtractor as tsx

# 1,000 series of 500 time-steps (float64)
X = np.random.randn(1000, 500)

# Extract 33 features (zero-copy, multi-threaded)
features = tsx.extract_features(X)

print("Output shape:", features.shape)      # (1000, 33)
print("Feature names:", tsx.feature_names()[:5])
# ['mean', 'std', 'var', 'min', 'max', ...]

2. Labeled Pandas DataFrame

# Returns a labeled pandas DataFrame with clean column headers
df = tsx.extract_features_df(X)
print(df.head())

3. Ragged Series of Different Lengths

# Sequences of varying lengths are supported natively
arr1 = np.random.randn(300)
arr2 = np.random.randn(500)
arr3 = np.random.randn(120)

features = tsx.extract_features([arr1, arr2, arr3])
print(features.shape)  # (3, 33)

4. Sliding Windows over a Long Signal

# Extract rolling window features from a 1D continuous sensor stream
signal = np.random.randn(100_000)
windowed_features = tsx.sliding_features(signal, window=256, stride=64)

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Scikit-Learn Pipeline

Integrate directly into standard classification, regression, or clustering pipelines:

import numpy as np
import tsxtractor as tsx
from sklearn.base import BaseEstimator, TransformerMixin
from sklearn.pipeline import Pipeline
from sklearn.ensemble import RandomForestClassifier
from sklearn.preprocessing import StandardScaler

class TsxtractTransformer(BaseEstimator, TransformerMixin):
    """Extract 33 Tsxtract features per input row (one series per row)."""
    def fit(self, X, y=None):
        return self
    def transform(self, X):
        X_contig = np.ascontiguousarray(X, dtype=np.float64)
        return tsx.extract_features(X_contig)

# Assemble end-to-end reproducible pipeline
pipeline = Pipeline([
    ("features", TsxtractTransformer()),
    ("scaler", StandardScaler()),
    ("classifier", RandomForestClassifier(n_estimators=100))
])

# Fit on raw time-series training data (n_samples, time_steps)
pipeline.fit(X_train, y_train)
y_pred = pipeline.predict(X_test)

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Streaming & Real-Time Telemetry

Maintain running statistical features in real-time embedded systems or trading loops without recomputing from scratch:

from tsxtractor import StreamingExtractor

# Initialize streaming extractor with buffer capacity
stream = StreamingExtractor(capacity=500)

# Ingest points one by one with O(1) state updates
for tick in incoming_data_feed:
    stream.push(tick)
    current_features = stream.compute()

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Benchmarks

Tested on a 16-core system across 1,000 series of 500 steps (500,000 data points total):

Library	Features	Runtime	Series / sec	Per-Feature Cost	Speedup vs Tsxtract
Tsxtract (tsxtract-rs)	33	1.25 ms	800,256	0.038 µs	Baseline (1.0×)
catch22	22	1,024.8 ms	976	46.58 µs	820× slower
TSFEL	156	7,154.0 ms	140	45.86 µs	5,725× slower
tsfresh	777	17,683.3 ms	57	22.76 µs	14,151× slower

Memory Footprint (100,000 series × 500 steps):

• Tsxtract: +25.2 MiB allocated memory (strictly the output matrix, zero input duplication).
• tsfresh / Pandas: +1,250 MiB memory ballooning due to melted DataFrame indices.

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The 33 Curated Features

Tsxtract deliberately computes 33 high-signal, non-redundant features spanning all temporal domains:

• Distribution Moments: Mean, Standard Deviation, Variance, Skewness, Kurtosis.
• Extrema & Spans: Min, Max, Peak-to-Peak Range, Quantiles (q05, q25, median, q75, q95), Interquartile Range (IQR).
• Dynamics & Crossing: Zero Crossing Rate, Mean Crossing Rate, Root Mean Square (RMS), Crest Factor, Median Absolute Deviation (MAD).
• Temporal Differences: Mean Absolute Change, Mean Consecutive Change, Number of Local Peaks.
• Autocorrelation Structure: Lag-1, Lag-2, Lag-3, Lag-5, Lag-10 Autocorrelation.
• Spectral Domain: Energy, Spectral Energy, Dominant Frequency, Spectral Centroid, Spectral Spread, Spectral Roll-off.
• Complexity: Permutation Entropy (order 3, delay 1).

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Contributing

Contributions, bug reports, and PRs are welcome! Please check CONTRIBUTING.md for details on setting up the local Rust/Python development environment and running the benchmark suites.

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License

Distributed under the MIT License. See LICENSE for details.

Built with 🦀 Rust & 🐍 Python by Aamod.