TFMe: Time–Frequency–Memory-Enhanced Transformer for Anomaly Detection

TFMe is a reconstruction-based anomaly detector for LiDAR time series collected in car-parking environments. It couples a dual-branch Transformer autoencoder — one branch operating in the time domain, one in the frequency domain — with a multi-head memory module that constrains reconstructions to patterns seen during normal-only training.

Because the model is trained exclusively on normal data and never learns to reconstruct anomalies, a sample is flagged when either branch fails to reconstruct it within a fixed error threshold.

Architecture

Component	Role
PositionalEncoding	Standard sinusoidal encoding applied to both branches
MultiHeadMemory	Learned memory bank queried by cosine similarity, with hard shrinkage to sparsify addressing
TimeBranch	Transformer encoder over the raw temporal sequence, followed by memory read-out and reconstruction
SpectralBranch	Real FFT of the input, Transformer encoder over frequency bins, memory read-out, inverse FFT back to the time domain
DualDomainTransformer	Wraps both branches; returns both reconstructions and both attention maps

Memory module

Each input embedding is split across n_heads and matched against a bank of MEMORY_SLOTS learned prototypes via cosine similarity. The resulting attention is softmaxed, then passed through a hard-shrinkage operator with threshold λ = 1 / MEMORY_SLOTS, which zeroes out weakly matched slots and re-normalizes the remainder. The reconstruction is a sparse convex combination of memory items, so inputs unlike anything in the training distribution cannot be reconstructed accurately.

Detection rule

Per-sample mean squared reconstruction error is computed independently for each branch and compared against a 3-sigma threshold calibrated on the training set:

Branch	Threshold
Time	0.007409659097902477
Frequency	0.005404344177804887

A sample is labelled anomalous if either branch exceeds its threshold.

Data

dataset.zip contains dataset.npy, a float32 array of shape (71045, 340). The first 305 columns hold the LiDAR features used by the model; the remaining columns are ignored by the notebook.

Each row is reshaped to (5, 61) — 5 consecutive timesteps × 61 azimuth bins — giving one sample per row. Features are scaled with a MinMaxScaler fitted on the training split only.

Split	Row range	Samples
Train	0 : 50335	50,335
Validation	50335 : 60341	10,006
Test	60341 : 71045	10,704

The test split contains normal samples only, so the evaluation in the notebook reports false positives and true negatives.

Configuration

Parameter	Value
N_LAYERS	4
D_MODEL	128
N_HEAD	8
FFN	512
MEMORY_SLOTS	250
TIMESTEPS	5
FEATURES	61
BATCH_SIZE	256
EPOCHS	500
LEARNING_RATE	1e-4

Training uses MSE reconstruction loss with early stopping (patience-based, restoring best weights).

Prerequisites

Conda is recommended for managing the environment.

python 3.8.20
pytorch 2.4.1+cu118
cudatoolkit 11.8.0
cudnn 9.10.1.4
numpy 1.24.1
scikit-learn 1.3.2

A CUDA device is used automatically when available; the notebook falls back to CPU otherwise.

Usage

1. Extract the dataset into the repository root:

   unzip dataset.zip

2. Open TFMe_Model_Architecture.ipynb and run the cells in order. The notebook loads and splits the data, defines the full architecture, restores the pretrained weights from best.pth, and evaluates the detector on the test split.

To use the model directly:

importtorchmodel=DualDomainTransformer()
model.load_state_dict(torch.load("best.pth", map_location=DEVICE))
model.to(DEVICE).eval()
withtorch.no_grad():
time_recon, freq_recon, time_att, freq_att=model(inputs) # inputs: (B, 5, 61)

Repository Contents

File	Description
TFMe_Model_Architecture.ipynb	Full model definition, pretrained-weight loading, and evaluation walkthrough
best.pth	Pretrained DualDomainTransformer checkpoint
dataset.zip	Compressed dataset.npy feature array