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DTW-SOM: Self-organizing map for time-series data

Project description

DTW-SOM: Self-organizing map for time-series data

The method DTW-SOM (Dynamic Time Warping Self-Organizing Map) was built for the paper Exploring time-series motifs through DTW-SOM. In the github repository, you'll find all the work presented in the paper.

DTW-SOM is a vanilla Self-Organizing Map with three main differences, namely (1) the use the Dynamic Time Warping distance instead of the Euclidean distance, (2) the adoption of two new network initialization routines (a random sample initialization and an anchor initialization) and (3) the adjustment of the Adaptation phase of the training to work with variable-length time-series sequences.

In the paper, we argue that visually exploring time-series motifs computed by motif discovery algorithms can be useful to understand and debug results and we propose the use of DTW-SOM on the list of motif’s centers to conduct these explorations. We then test DTW-SOM in a synthetic motif dataset and a real time-series dataset called GunPoint. After an exploration of results, we conclude that DTW-SOM is capable of extracting relevant information from a set of motifs and display it in a visualization that is space-efficient.

Github project structure

├── notebooks          <- Jupyter notebooks use to test dtwsom and to run the anlsysis for the paper (including the plots)
├── paper              <- Folder the the PDF and latex project for the paper
├── src                <- Folder with the dtwsom module
├──          <- The top-level README for this project
├── requirements.txt   <- The requirements file for reproducing the environment used in the paper




This packages is available on PyPI and thus can be directly installed with pip:

pip install dtw_som

Alternatively, this package can installed from source by cloning this repository and installing it manually with the command:

python install

Example Code

Import packages and generate a dummy dataset with 2 clusters, a noisy sine curve and a noise line centered at 10:

import dtwsom
import math
import random
import numpy as np
import matplotlib.pyplot as plt
from pyclustering.nnet.som import type_conn

def gen_noisy_sine_list(f0, fs, mean_dur, size):
    final_list = []
    for i in range(size):
        dur = random.sample([mean_dur-1, mean_dur, mean_dur+1], 1)[0]
        t = np.arange(dur)
        sinusoid = np.sin(2*np.pi*t*(f0/fs))
        noise = np.random.normal(0,0.3, dur)
        noisy_sinusoid = noise + sinusoid
    return final_list

def gen_noisy_list(mean_dur, size):
    final_list = []
    for i in range(size):
        dur = random.sample([mean_dur-1, mean_dur, mean_dur+1], 1)[0]
        noise = np.random.normal(0,0.3, dur)+10
    return final_list

sin_dataset = gen_noisy_sine_list(1, 10, 25, 50) + gen_noisy_list(20, 50)

Define and train the network:

rows = 3
cols = 3
structure = type_conn.grid_four
network = dtwsom.DtwSom(rows, cols, structure)

network.train(sin_dataset, 20)

After training, you can visualise the U-matrix the Winner matrix:


Finally, you can also visualize the each unit as a time-series:

n_neurons = network._size
fig, axs = plt.subplots(3,3,figsize=(20, 10), sharey=True)
for neuron_index in range(n_neurons):
    col = math.floor(neuron_index/3)
    row = neuron_index % 3
    neuron_weights = network._weights[neuron_index]
    axs[row, col].plot(np.arange(len(neuron_weights)), neuron_weights, label=str(neuron_index))
    axs[row, col].set_ylabel("Neuron: "+str(neuron_index))

To confirm the output of this example, check the following notebook.

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