Time Series Classification for Production

Time Series Classification, Feature Engineering, Production ML, Edge Computing, IoT, Algorithm Adaptation
Time Series
Production ML
Feature Engineering
Algorithm Adaptation
PyTorch
Scikit-Learn
Real-time ML
Published

November 1, 2025

Modified

November 1, 2025

OUTCOME: State-of-the-art accuracy with 10-100× faster inference than deep learning through modern feature engineering and careful algorithm selection.

Why this matters

Many real‑world systems need accurate, low‑latency models on constrained hardware (edge, IoT, embedded). Deep learning models can be heavy to train and serve; feature‑based methods deliver strong accuracy with simpler, faster inference.

Approach in brief

  • Understand constraints (latency, resources, data)
  • Pick the right classifier (ex: ROCKET variants with Ridge) for the job
  • Optimize features and classifier for your use case

ROCKET family - example of efficient and accurate classifiers

  • Thousands of simple, randomly‑parameterized convolutions over the series
  • Pool to compact features; train a lightweight linear classifier
  • Results: competitive accuracy on many benchmarks and often 10–100× faster inference than deep architectures; MiniROCKET is deterministic and reproducible

Variants at a glance

  • MiniROCKET — Deterministic kernels and PPV pooling. Best for edge/IoT and reproducibility.
  • MultiROCKET — Multiple pooling stats; raw + differenced signals. Best for complex multivariate data when accuracy matters.
  • ROCKET — Random kernels with simple pooling. Solid, fast baseline.

Simple pipeline

from rocket_and_friends.src.mini_rocket import MiniROCKET
from sklearn.linear_model import RidgeClassifierCV
import numpy as np

# Simple, production-ready pipeline
transformer = MiniROCKET(num_features=10_000, random_state=42)
transformer.fit(X_train)

X_train_features = transformer.transform(X_train)
X_test_features = transformer.transform(X_test)

classifier = RidgeClassifierCV(alphas=np.logspace(-3, 3, 7))
classifier.fit(X_train_features, y_train)
y_pred = classifier.predict(X_test_features)

Real-world use cases

  • Edge anomaly detection: Sub‑100 ms latency on embedded hardware; consistent behavior across a fleet.
  • IoT sensors: Efficient CPU‑only processing that preserves battery life.
  • Industrial monitoring: High‑quality multichannel vibration classification with interpretable features.

How I can help

  • Algorithm selection and adaptation
  • Custom optimization for your data and constraints
  • Production deployment and MLOps integration
  • Training and knowledge transfer

Contact

Farrukh Nauman | AI Consultant
Time Series Classification, Forecasting, Physics-Informed ML | PhD

📧 farrukhnordicworks@gmail.com
🔗 LinkedIn: fnauman
💻 GitHub: fnauman

NoteLearn More

Curious how these techniques could improve your time series applications? Let’s discuss your constraints and goals.