Clustering Consuming Behaviors in Time: A Machine Studying Method to Preventive Well being

It’s nicely that what we eat issues — however what if when and how usually we eat issues simply as a lot?

Within the midst of ongoing scientific debate round the advantages of intermittent fasting, this query turns into much more intriguing. As somebody enthusiastic about machine studying and wholesome dwelling, I used to be impressed by a 2017 analysis paper[1] exploring this intersection. The authors launched a novel distance metric referred to as Modified Dynamic Time Warping (MDTW) — a method designed to account not just for the dietary content material of meals but additionally their timing all through the day.

Motivated by their work[1], I constructed a full implementation of MDTW from scratch utilizing Python. I utilized it to cluster simulated people into temporal dietary patterns, uncovering distinct behaviors like skippers, snackers, and night time eaters.

Whereas MDTW might sound like a distinct segment metric, it fills a important hole in time-series comparability. Conventional distance measures — corresponding to Euclidean distance and even classical Dynamic Time Warping (DTW) — wrestle when utilized to dietary knowledge. Folks don’t eat at fastened occasions or with constant frequency. They skip meals, snack irregularly, or eat late at night time.

MDTW is designed for precisely this sort of temporal misalignment and behavioral variability. By permitting versatile alignment whereas penalizing mismatches in each nutrient content material and meal timing, MDTW reveals delicate however significant variations in how folks eat.

What this text covers:

1. Mathematical basis of MDTW — defined intuitively.
2. From formulation to code — implementing MDTW in Python with dynamic programming.
3. Producing artificial dietary knowledge to simulate real-world consuming conduct.
4. Constructing a distance matrix between particular person consuming data.
5. Clustering people with Ok-Medoids and evaluating with silhouette and elbow strategies.
6. Visualizing clusters as scatter plots and joint distributions.
7. Decoding temporal patterns from clusters: who eats when and the way a lot?

Fast Observe on Classical Dynamic Time Warping (DTW)

Dynamic Time Warping (DTW) is a traditional algorithm used to measure similarity between two sequences that will fluctuate in size or timing. It’s extensively utilized in speech recognition, gesture evaluation, and time sequence alignment. Let’s see a quite simple instance of the Sequence A is aligned to Sequence B (shifted model of B) with utilizing conventional dynamic time warping algorithm utilizing fastdtw library. As enter, we give a distance metric as Euclidean. Additionally, we put time sequence to calculate the gap between these time sequence and optimized aligned path.

import numpy as np
import matplotlib.pyplot as plt
from fastdtw import fastdtw
from scipy.spatial.distance import euclidean
# Pattern sequences (scalar values)
x = np.linspace(0, 3 * np.pi, 30)
y1 = np.sin(x)
y2 = np.sin(x+0.5)  # Shifted model
# Convert scalars to vectors (1D)
y1_vectors = [[v] for v in y1]
y2_vectors = [[v] for v in y2]
# Use absolute distance for scalars
distance, path = fastdtw(y1_vectors, y2_vectors, dist=euclidean)
#or for scalar 
# distance, path = fastdtw(y1, y2, dist=lambda x, y: np.abs(x-y))

distance, path = fastdtw(y1, y2,dist=lambda x, y: np.abs(x-y))
# Plot the alignment
plt.determine(figsize=(10, 4))
plt.plot(y1, label='Sequence A (sluggish)')
plt.plot(y2, label='Sequence B (shifted)')

# Draw alignment strains
for (i, j) in path:
    plt.plot([i, j], [y1[i], y2[j]], shade='grey', linewidth=0.5)

plt.title(f'Dynamic Time Warping Alignment (Distance = {distance:.2f})')
plt.xlabel('Time Index')
plt.legend()
plt.tight_layout()
plt.savefig('dtw_alignment.png')
plt.present()

The trail returned by fastdtw (or any DTW algorithm) is a sequence of index pairs (i, j) that characterize the optimum alignment between two time sequence. Every pair signifies that aspect A[i] is matched with B[j]. By summing the distances between all these matched pairs, the algorithm computes the optimized cumulative price — the minimal complete distance required to warp one sequence to the opposite.

Modified Dynamic Warping

The important thing problem when making use of dynamic time warping (DTW) to dietary knowledge (vs. easy examples like sine waves or fixed-length sequences) lies within the complexity and variability of real-world consuming behaviors. Some challenges and the proposed resolution within the paper[1] as a response to every problem are as follows:

1. Irregular Time Steps: MDTW accounts for this by explicitly incorporating the time distinction within the distance perform.
2. Multidimensional Vitamins: MDTW helps multidimensional vectors to characterize vitamins corresponding to energy, fats and so on. and makes use of a weight matrix to deal with differing items and the significance of vitamins,
3. Unequal variety of meals: MDTW permits for matching with empty consuming occasions, penalizing skipped or unmatched meals appropriately.
4. Time Sensitivity: MDTW contains a time distinction penalty, weighting consuming occasions far aside in time even when the vitamins are related.

Consuming Event Knowledge Illustration

In line with the modified dynamic time warping proposed within the paper[1], every individual’s weight-reduction plan will be regarded as a sequence of consuming occasions, the place every occasion has:

As an example how consuming data seem in actual knowledge, I created three artificial dietary profiles solely contemplating calorie consumption — Skipper, Night time Eater, and Snacker. Let’s assume if we ingest the uncooked knowledge from an API on this format:

skipper={
    'person_id': 'skipper_1',
    'data': [
        {'time': 12, 'nutrients': [300]},  # Skipped breakfast, massive lunch
        {'time': 19, 'vitamins': [600]},  # Giant dinner
    ]
}
night_eater={
    'person_id': 'night_eater_1',
    'data': [
        {'time': 9, 'nutrients': [150]},   # Mild breakfast
        {'time': 14, 'vitamins': [250]},  # Small lunch
        {'time': 22, 'vitamins': [700]},  # Giant late dinner
    ]
}
snacker=  {
    'person_id': 'snacker_1',
    'data': [
        {'time': 8, 'nutrients': [100]},   # Mild morning snack
        {'time': 11, 'vitamins': [150]},  # Late morning snack
        {'time': 14, 'vitamins': [200]},  # Afternoon snack
        {'time': 17, 'vitamins': [100]},  # Early night snack
        {'time': 21, 'vitamins': [200]},  # Night time snack
    ]
}
raw_data = [skipper, night_eater, snacker]

As instructed within the paper, the dietary values must be normalized by the full calorie consumptions.

import numpy as np
import matplotlib.pyplot as plt
def create_time_series_plot(knowledge,save_path=None):
    plt.determine(figsize=(10, 5))
    for individual,document in knowledge.gadgets():
        #in case the nutrient vector has multiple dimension
        knowledge=[[time, float(np.mean(np.array(value)))] for time,worth in document.gadgets()]

        time = [item[0] for merchandise in knowledge]
        nutrient_values = [item[1] for merchandise in knowledge]
        # Plot the time sequence
        plt.plot(time, nutrient_values, label=individual, marker='o')

    plt.title('Time Sequence Plot for Nutrient Knowledge')
    plt.xlabel('Time')
    plt.ylabel('Normalized Nutrient Worth')
    plt.legend()
    plt.grid(True)
    if save_path:
        plt.savefig(save_path)

def prepare_person(individual):
    
    # Verify if all vitamins have similar size
    nutrients_lengths = [len(record['nutrients']) for document in individual["records"]]
    
    if len(set(nutrients_lengths)) != 1:
        increase ValueError(f"Inconsistent nutrient vector lengths for individual {individual['person_id']}.")

    sorted_records = sorted(individual["records"], key=lambda x: x['time'])

    vitamins = np.stack([np.array(record['nutrients']) for document in sorted_records])
    total_nutrients = np.sum(vitamins, axis=0)

    # Verify to keep away from division by zero
    if np.any(total_nutrients == 0):
        increase ValueError(f"Zero complete vitamins for individual {individual['person_id']}.")

    normalized_nutrients = vitamins / total_nutrients

    # Return a dictionary {time: [normalized nutrients]}
    person_dict = {
        document['time']: normalized_nutrients[i].tolist()
        for i, document in enumerate(sorted_records)
    }

    return person_dict
prepared_data = {individual['person_id']: prepare_person(individual) for individual in raw_data}
create_time_series_plot(prepared_data)

Calculation Distance of Pairs

The computation of distance measure between pair of people are outlined within the formulation under. The primary time period characterize an Euclidean distance of nutrient vectors whereas the second takes under consideration the time penalty.

This formulation is applied within the local_distance perform with the instructed values:

import numpy as np

def local_distance(eo_i, eo_j,delta=23, beta=1, alpha=2):
    """
    Calculate the native distance between two occasions.
    Args:
        eo_i (tuple): Occasion i (time, vitamins).
        eo_j (tuple): Occasion j (time, vitamins).
        delta (float): Time scaling issue.
        beta (float): Weighting issue for time distinction.
        alpha (float): Exponent for time distinction scaling.
    Returns:
        float: Native distance.
    """
    ti, vi = eo_i
    tj, vj = eo_j
   
    vi = np.array(vi)
    vj = np.array(vj)

    if vi.form != vj.form:
        increase ValueError("Mismatch in function dimensions.")
    if np.any(vi < 0) or np.any(vj < 0):
        increase ValueError("Nutrient values should be non-negative.")
    if np.any(vi>1 ) or np.any(vj>1):
        increase ValueError("Nutrient values should be within the vary [0, 1].")   
    W = np.eye(len(vi))  # Assume W = id for now
    value_diff = (vi - vj).T @ W @ (vi - vj) 
    time_diff = (np.abs(ti - tj) / delta) ** alpha
    scale = 2 * beta * (vi.T @ W @ vj)
    distance = value_diff + scale * time_diff
  
    return distance

We assemble an area distance matrix deo(i,j) for every pair of people being in contrast. The variety of rows and columns on this matrix corresponds to the variety of consuming events for every particular person.

As soon as the native distance matrix deo(i,j) is constructed — capturing the pairwise distances between all consuming events of two people — the following step is to compute the world price matrix dER(i,j). This matrix accumulates the minimal alignment price by contemplating three attainable transitions at every step: matching two consuming events, skipping an event within the first document (aligning to an empty), or skipping an event within the second document.

To compute the total distance between two sequences of consuming events, we construct:

A native distance matrix deo stuffed utilizing local_distance.

• A world price matrix dER utilizing dynamic programming, minimizing over:
• Match
• Skip within the first sequence (align to empty)
• Skip within the second sequence

These straight implement the recurrence:

import numpy as np

def mdtw_distance(ER1, ER2, delta=23, beta=1, alpha=2):
    """
    Calculate the modified DTW distance between two sequences of occasions.
    Args:
        ER1 (record): First sequence of occasions (time, vitamins).
        ER2 (record): Second sequence of occasions (time, vitamins).
        delta (float): Time scaling issue.
        beta (float): Weighting issue for time distinction.
        alpha (float): Exponent for time distinction scaling.
    
    Returns:
        float: Modified DTW distance.
    """
    m1 = len(ER1)
    m2 = len(ER2)
   
    # Native distance matrix together with matching with empty
    deo = np.zeros((m1 + 1, m2 + 1))

    for i in vary(m1 + 1):
        for j in vary(m2 + 1):
            if i == 0 and j == 0:
                deo[i, j] = 0
            elif i == 0:
                tj, vj = ER2[j-1]
                deo[i, j] = np.dot(vj, vj)  
            elif j == 0:
                ti, vi = ER1[i-1]
                deo[i, j] = np.dot(vi, vi)
            else:
                deo[i, j]=local_distance(ER1[i-1], ER2[j-1], delta, beta, alpha)

    # # World price matrix
    dER = np.zeros((m1 + 1, m2 + 1))
    dER[0, 0] = 0

    for i in vary(1, m1 + 1):
        dER[i, 0] = dER[i-1, 0] + deo[i, 0]
    for j in vary(1, m2 + 1):
        dER[0, j] = dER[0, j-1] + deo[0, j]

    for i in vary(1, m1 + 1):
        for j in vary(1, m2 + 1):
            dER[i, j] = min(
                dER[i-1, j-1] + deo[i, j],   # Match i and j
                dER[i-1, j] + deo[i, 0],     # Match i to empty
                dER[i, j-1] + deo[0, j]      # Match j to empty
            )
   
    
    return dER[m1, m2]  # Return the ultimate price

ERA = record(prepared_data['skipper_1'].gadgets())
ERB = record(prepared_data['night_eater_1'].gadgets())
distance = mdtw_distance(ERA, ERB)
print(f"Distance between skipper_1 and night_eater_1: {distance}")

From Pairwise Comparisons to a Distance Matrix

As soon as we outline methods to calculate the gap between two people’ consuming patterns utilizing MDTW, the following pure step is to compute distances throughout the total dataset. To do that, we assemble a distance matrix the place every entry (i,j) represents the MDTW distance between individual i and individual j.

That is applied within the perform under:

import numpy as np

def calculate_distance_matrix(prepared_data):
    """
    Calculate the gap matrix for the ready knowledge.
    
    Args:
        prepared_data (dict): Dictionary containing ready knowledge for every individual.
        
    Returns:
        np.ndarray: Distance matrix.
    """
    n = len(prepared_data)
    distance_matrix = np.zeros((n, n))
    
    # Compute pairwise distances
    for i, (id1, records1) in enumerate(prepared_data.gadgets()):
        for j, (id2, records2) in enumerate(prepared_data.gadgets()):
            if i < j:  # Solely higher triangle
                print(f"Calculating distance between {id1} and {id2}")
                ER1 = record(records1.gadgets())
                ER2 = record(records2.gadgets())
                
                distance_matrix[i, j] = mdtw_distance(ER1, ER2)
                distance_matrix[j, i] = distance_matrix[i, j]  # Symmetric matrix
                
    return distance_matrix
def plot_heatmap(matrix,people_ids,save_path=None):
    """
    Plot a heatmap of the gap matrix.  
    Args:
        matrix (np.ndarray): The gap matrix.
        title (str): The title of the plot.
        save_path (str): Path to save lots of the plot. If None, the plot is not going to be saved.
    """
    plt.determine(figsize=(8, 6))
    plt.imshow(matrix, cmap='scorching', interpolation='nearest')
    plt.colorbar()
  
    plt.xticks(ticks=vary(len(matrix)), labels=people_ids)
    plt.yticks(ticks=vary(len(matrix)), labels=people_ids)
    plt.xticks(rotation=45)
    plt.yticks(rotation=45)
    if save_path:
        plt.savefig(save_path)
    plt.title('Distance Matrix Heatmap')

distance_matrix = calculate_distance_matrix(prepared_data)
plot_heatmap(distance_matrix, record(prepared_data.keys()), save_path='distance_matrix.png')

After computing the pairwise Modified Dynamic Time Warping (MDTW) distances, we will visualize the similarities and variations between people’ dietary patterns utilizing a heatmap. Every cell (i,j) within the matrix represents the MDTW distance between individual i and individual j— decrease values point out extra related temporal consuming profiles.

This heatmap affords a compact and interpretable view of dietary dissimilarities, making it simpler to determine clusters of comparable consuming behaviors.

This means that skipper_1 shares extra similarity with night_eater_1 than with snacker_1. The reason being that each skipper and night time eater have fewer, bigger meals concentrated later within the day, whereas the snacker distributes smaller meals extra evenly throughout your complete timeline.

Clustering Temporal Dietary Patterns

After calculating the pairwise distances utilizing Modified Dynamic Time Warping (MDTW), we’re left with a distance matrix that displays how dissimilar every particular person’s consuming sample is from the others. However this matrix alone doesn’t inform us a lot at a look — to disclose construction within the knowledge, we have to go one step additional.

Earlier than making use of any Clustering Algorithm, we first want a dataset that displays reasonable dietary behaviors. Since entry to large-scale dietary consumption datasets will be restricted or topic to utilization restrictions, I generated artificial consuming occasion data that simulate numerous day by day patterns. Every document represents an individual’s calorie consumption at particular hours all through a 24-hour interval.

import numpy as np

def generate_synthetic_data(num_people=5, min_meals=1, max_meals=5,min_calories=200,max_calories=800):
    """
    Generate artificial knowledge for a given variety of folks.
    Args:
        num_people (int): Variety of folks to generate knowledge for.
        min_meals (int): Minimal variety of meals per individual.
        max_meals (int): Most variety of meals per individual.
        min_calories (int): Minimal energy per meal.
        max_calories (int): Most energy per meal.
    Returns:
        record: Record of dictionaries containing artificial knowledge for every individual.
    """
    knowledge = []
    np.random.seed(42)  # For reproducibility
    for person_id in vary(1, num_people + 1):
        num_meals = np.random.randint(min_meals, max_meals + 1)  # random variety of meals between min and max
        meal_times = np.type(np.random.selection(vary(24), num_meals, exchange=False))  # random occasions sorted

        raw_calories = np.random.randint(min_calories, max_calories, dimension=num_meals)  # random energy between min and max

        person_record = {
            'person_id': f'person_{person_id}',
            'data': [
                {'time': float(time), 'nutrients': [float(cal)]} for time, cal in zip(meal_times, raw_calories)
            ]
        }

        knowledge.append(person_record)
    return knowledge

raw_data=generate_synthetic_data(num_people=1000, min_meals=1, max_meals=5,min_calories=200,max_calories=800)
prepared_data = {individual['person_id']: prepare_person(individual) for individual in raw_data}
distance_matrix = calculate_distance_matrix(prepared_data)

Selecting the Optimum Variety of Clusters

To find out the suitable variety of clusters for grouping dietary patterns, I evaluated two fashionable strategies: the Elbow Technique and the Silhouette Rating.

• The Elbow Technique analyzes the clustering price (inertia) because the variety of clusters will increase. As proven within the plot, the fee decreases sharply as much as 4 clusters, after which the speed of enchancment slows considerably. This “elbow” suggests diminishing returns past 4 clusters.
• The Silhouette Rating, which measures how nicely every object lies inside its cluster, confirmed a comparatively excessive rating at 4 clusters (≈0.50), even when it wasn’t absolutely the peak.

The next code computes the clustering price and silhouette scores for various values of okay (variety of clusters), utilizing the Ok-Medoids algorithm and a precomputed distance matrix derived from the MDTW metric:

from sklearn.metrics import silhouette_score
from sklearn_extra.cluster import KMedoids
import matplotlib.pyplot as plt

prices = []
silhouette_scores = []
for okay in vary(2, 10):
    mannequin = KMedoids(n_clusters=okay, metric='precomputed', random_state=42)
    labels = mannequin.fit_predict(distance_matrix)
    prices.append(mannequin.inertia_)
    rating = silhouette_score(distance_matrix, mannequin.labels_, metric='precomputed')
    silhouette_scores.append(rating)

# Plot
ks = record(vary(2, 10))
fig, ax1 = plt.subplots(figsize=(8, 5))

color1 = 'tab:blue'
ax1.set_xlabel('Variety of Clusters (okay)')
ax1.set_ylabel('Price (Inertia)', shade=color1)
ax1.plot(ks, prices, marker='o', shade=color1, label='Price')
ax1.tick_params(axis='y', labelcolor=color1)

# Create a second y-axis that shares the identical x-axis
ax2 = ax1.twinx()
color2 = 'tab:crimson'
ax2.set_ylabel('Silhouette Rating', shade=color2)
ax2.plot(ks, silhouette_scores, marker='s', shade=color2, label='Silhouette Rating')
ax2.tick_params(axis='y', labelcolor=color2)

# Non-compulsory: mix legends
lines1, labels1 = ax1.get_legend_handles_labels()
lines2, labels2 = ax2.get_legend_handles_labels()
ax1.legend(lines1 + lines2, labels1 + labels2, loc='higher proper')
ax1.vlines(x=4, ymin=min(prices), ymax=max(prices), shade='grey', linestyle='--', linewidth=0.5)

plt.title('Price and Silhouette Rating vs Variety of Clusters')
plt.tight_layout()
plt.savefig('clustering_metrics_comparison.png')
plt.present()

Decoding the Clustered Dietary Patterns

As soon as the optimum variety of clusters (okay=4) was decided, every particular person within the dataset was assigned to one among these clusters utilizing the Ok-Medoids mannequin. Now, we have to perceive what characterizes every cluster.

To take action, I adopted the method instructed within the unique MDTW paper [1]: analyzing the largest consuming event for each particular person, outlined by each the time of day it occurred and the fraction of complete day by day consumption it represented. This offers perception into when folks eat probably the most energy and how a lot they eat throughout that peak event.

# Kmedoids clustering with the optimum variety of clusters
from sklearn_extra.cluster import KMedoids
import seaborn as sns
import pandas as pd

okay=4
mannequin = KMedoids(n_clusters=okay, metric='precomputed', random_state=42)
labels = mannequin.fit_predict(distance_matrix)

# Discover the time and fraction of their largest consuming event
def get_largest_event(document):
    complete = sum(v[0] for v in document.values())
    largest_time, largest_value = max(document.gadgets(), key=lambda x: x[1][0])
    fractional_value = largest_value[0] / complete if complete > 0 else 0
    return largest_time, fractional_value

# Create a largest meal knowledge per cluster
data_per_cluster = {i: [] for i in vary(okay)}
for i, person_id in enumerate(prepared_data.keys()):
    cluster_id = labels[i]
    t, v = get_largest_event(prepared_data[person_id])
    data_per_cluster[cluster_id].append((t, v))

import seaborn as sns
import matplotlib.pyplot as plt
import pandas as pd

# Convert to pandas DataFrame
rows = []
for cluster_id, values in data_per_cluster.gadgets():
    for hour, fraction in values:
        rows.append({"Hour": hour, "Fraction": fraction, "Cluster": f"Cluster {cluster_id}"})
df = pd.DataFrame(rows)
plt.determine(figsize=(10, 6))
sns.scatterplot(knowledge=df, x="Hour", y="Fraction", hue="Cluster", palette="tab10")
plt.title("Consuming Occasions Throughout Clusters")
plt.xlabel("Hour of Day")
plt.ylabel("Fraction of Every day Consumption (largest meal)")
plt.grid(True)
plt.tight_layout()
plt.present()

Whereas the scatter plot affords a broad overview, a extra detailed understanding of every cluster’s consuming conduct will be gained by inspecting their joint distributions.
By plotting the joint histogram of the hour and fraction of day by day consumption for the most important meal, we will determine attribute patterns, utilizing the code under:

# Plot every cluster utilizing seaborn.jointplot
for cluster_label in df['Cluster'].distinctive():
    cluster_data = df[df['Cluster'] == cluster_label]
    g = sns.jointplot(
        knowledge=cluster_data,
        x="Hour",
        y="Fraction",
        variety="scatter",
        peak=6,
        shade=sns.color_palette("deep")[int(cluster_label.split()[-1])]
    )
    g.fig.suptitle(cluster_label, fontsize=14)
    g.set_axis_labels("Hour of Day", "Fraction of Every day Consumption (largest meal)", fontsize=12)
    g.fig.tight_layout()
    g.fig.subplots_adjust(high=0.95)  # modify title spacing
    plt.present()

To know how people have been distributed throughout clusters, I visualized the variety of folks assigned to every cluster. The bar plot under reveals the frequency of people grouped by their temporal dietary sample. This helps assess whether or not sure consuming behaviors — corresponding to skipping meals, late-night consuming, or frequent snacking — are extra prevalent within the inhabitants.

Based mostly on the joint distribution plots, distinct temporal dietary behaviors emerge throughout clusters:

Cluster 0 (Versatile or Irregular Eater) reveals a broad dispersion of the most important consuming events throughout each the 24-hour day and the fraction of day by day caloric consumption.

Cluster 1 (Frequent Mild Eaters) shows a extra evenly distributed consuming sample, the place no single consuming event exceeds 30% of the full day by day consumption, reflecting frequent however smaller meals all through the day. That is the cluster that probably represents “regular eaters” — those that eat three comparatively balanced meals unfold all through the day. That’s due to low variance in timing and fraction per consuming occasion.

Cluster 2 (Early Heavy Eaters) is outlined by a really distinct and constant sample: people on this group eat nearly their total day by day caloric consumption (near 100%) in a single meal, predominantly through the early hours of the day (midnight to midday).

Cluster 3 (Late Night time Heavy Eaters) is characterised by people who eat practically all of their day by day energy in a single meal through the late night or night time hours (between 6 PM and midnight). Like Cluster 2, this group reveals a unimodal consuming sample with a very excessive fractional consumption (~1.0), indicating that almost all members eat as soon as per day, however in contrast to Cluster 2, their consuming window is considerably delayed.

CONCLUSION

On this venture, I explored how Modified Dynamic Time Warping (MDTW) will help uncover temporal dietary patterns — focusing not simply on what we eat, however when and how a lot. Utilizing artificial knowledge to simulate reasonable consuming behaviors, I demonstrated how MDTW can cluster people into distinct profiles like irregular or versatile eaters, frequent mild eaters, early heavy eaters and later night time eaters primarily based on the timing and magnitude of their meals.

Whereas the outcomes present that MDTW mixed with Ok-Medoids can reveal significant patterns in consuming behaviors, this method isn’t with out its challenges. Because the dataset was synthetically generated and clustering was primarily based on a single initialization, there are a number of caveats value noting:

• The clusters seem messy, presumably as a result of the artificial knowledge lacks robust, naturally separable patterns — particularly if meal occasions and calorie distributions are too uniform.
• Some clusters overlap considerably, significantly Cluster 0 and Cluster 1, making it tougher to tell apart between actually totally different behaviors.
• With out labeled knowledge or anticipated floor reality, evaluating cluster high quality is troublesome. A possible enchancment can be to inject identified patterns into the dataset to check whether or not the clustering algorithm can reliably get well them.

Regardless of these limitations, this work reveals how a nuanced distance metric — designed for irregular, real-life patterns — can floor insights conventional instruments might overlook. The methodology will be prolonged to personalised well being monitoring, or any area the place when issues occur issues simply as a lot as what occurs.

I’d love to listen to your ideas on this venture — whether or not it’s suggestions, questions, or concepts for the place MDTW may very well be utilized subsequent. That is very a lot a piece in progress, and I’m at all times excited to be taught from others.

Should you discovered this handy, have concepts for enhancements, or wish to collaborate, be at liberty to open a problem or ship a Pull Request on GitHub. Contributions are greater than welcome!

Thanks a lot for studying all the best way to the top — it actually means loads.

Code on GitHub : https://github.com/YagmurGULEC/mdtw-time-series-clustering

REFERENCES

[1] Khanna, Nitin, et al. “Modified dynamic time warping (MDTW) for estimating temporal dietary patterns.” 2017 IEEE World Convention on Sign and Data Processing (GlobalSIP). IEEE, 2017.