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PyPI version Arxiv NeurIPS License: MIT

Segment, Shuffle, and Stitch: A Simple Layer for Improving Time-Series Representations

Shivam Grover        Amin Jalali        Ali Etemad

NeurIPS 2024

[Paper]

Overview

S3 is a simple plug-and-play neural network component designed to enhance time-series representation learning. S3 works by segmenting the input time-series, shuffling the segments in a learned, task-specific manner, and stitching the shuffled segments back together. S3 is modular and can be stacked to create varying degrees of granularity, integrating seamlessly with many neural architectures (e.g., CNNs, Transformers) with minimal computational overhead. It has shown improvements in both time-series classification and forecasting tasks.

Key Features

  • Segment-Shuffle-Stitch: Segment the input time-series, shuffle segments based on learned parameters, and stitch them back together with the original sequence for enhanced learning.
  • Easy to use: With just 2 lines of code, you can integrate multiple S3 layers into your model.
  • Lightweight: S3 adds minimal computational overhead to existing models.
  • Versatile: Integrates easily with various types of models, including CNNs, Transformers, and others.

Installation

You can install S3 via PyPI:

pip install s3-timeseries

Or install from the source:

git clone https://github.com/shivam-grover/S3-TimeSeries.git
cd S3
pip install .

Usage

Here's how to incorporate S3 into your PyTorch model:

import torch
from S3 import S3

# Sample input: batch_size = 32, time_steps = 96, features = 9
x = torch.randn(32, 96, 9)

# Initialize S3 with your desired configuration
s3_layers = S3(num_layers=3, initial_num_segments=4, shuffle_vector_dim=1, segment_multiplier=2)

# Apply the S3 layer
output = s3_layers(x)

Arguments for S3

Parameter Explanation Range / Sample Values
num_layers Number of S3 layers to stack. Positive integer (e.g., 1, 2, 3)
initial_num_segments Number of segments in the first layer. This will be used only if segments_per_layer is not provided. Positive integer (e.g., 4, 8, 16)
segment_multiplier Multiplier for the number of segments in each consecutive layer. Positive float or integer (e.g., 0.5, 1, 2)
shuffle_vector_dim Dimensionality of the shuffle vector, controlling shuffle complexity. Positive integer (e.g., 1, 2, 3)
use_conv_w_avg Whether to use convolution-based weighted averaging. True, False
initialization_type Optional. Initialization type for shuffle vectors, such as "kaiming" or "manual". "kaiming", "manual"
use_stitch Optional. Whether to use stitching to combine shuffled and original sequences. True, False
segments_per_layer TODO. An array specifying the exact number of segments for each layer. Overrides initial_num_segments and segment_multiplier. List of integers (e.g., [4, 8, 16])

Citation

If you find this repository useful, please consider giving a star and citing it using the given BibTeX entry:

@inproceedings{
      S3TimeSeries,
      title={Segment, Shuffle, and Stitch: A Simple Layer for Improving Time-Series Representations},
      author={Shivam Grover, Amin Jalali, Ali Etemad},
      booktitle={Neural Information Processing Systems (NeurIPS)},
      year={2024},
      url={https://arxiv.org/pdf/2405.20082}
}

Contact

Please contact me at shivam.grover@queensu.ca or connect with me on LinkedIn.