Clustering Eating Behaviors in Time: A Machine Learning Approach to Preventive Health

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 keen about machine studying and wholesome residing, I used to be impressed by a 2017 analysis paper[1] exploring this intersection. The authors launched a novel distance metric known 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 evening eaters.

Whereas MDTW could 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) — battle when utilized to dietary knowledge. Individuals don’t eat at mounted occasions or with constant frequency. They skip meals, snack irregularly, or eat late at evening.

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 refined however significant variations in how individuals eat.

What this text covers:

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

Fast Notice 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 collection 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 collection to calculate the gap between these time collection 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 traces
for (i, j) in path:
    plt.plot([i, j], [y1[i], y2[j]], coloration='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 symbolize the optimum alignment between two time collection. 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 value — 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 symbolize vitamins corresponding to energy, fats and so forth. and makes use of a weight matrix to deal with differing models 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 particular person’s eating regimen will be regarded as a sequence of consuming occasions, the place every occasion has:

For example how consuming information 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',
    'information': [
        {'time': 12, 'nutrients': [300]},  # Skipped breakfast, massive lunch
        {'time': 19, 'vitamins': [600]},  # Massive dinner
    ]
}
night_eater={
    'person_id': 'night_eater_1',
    'information': [
        {'time': 9, 'nutrients': [150]},   # Mild breakfast
        {'time': 14, 'vitamins': [250]},  # Small lunch
        {'time': 22, 'vitamins': [700]},  # Massive late dinner
    ]
}
snacker=  {
    'person_id': 'snacker_1',
    'information': [
        {'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 steered within the paper, the dietary values ought to 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 particular person,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 collection
        plt.plot(time, nutrient_values, label=particular person, marker='o')

    plt.title('Time Collection 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(particular person):
    
    # Test if all vitamins have similar size
    nutrients_lengths = [len(record['nutrients']) for document in particular person["records"]]
    
    if len(set(nutrients_lengths)) != 1:
        increase ValueError(f"Inconsistent nutrient vector lengths for particular person {particular person['person_id']}.")

    sorted_records = sorted(particular person["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)

    # Test to keep away from division by zero
    if np.any(total_nutrients == 0):
        increase ValueError(f"Zero complete vitamins for particular person {particular person['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 = {particular person['person_id']: prepare_person(particular person) for particular person 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 components beneath. The primary time period symbolize an Euclidean distance of nutrient vectors whereas the second takes into consideration the time penalty.

This components is applied within the local_distance perform with the steered 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 have to be non-negative.")
    if np.any(vi>1 ) or np.any(vj>1):
        increase ValueError("Nutrient values have to be within the vary [0, 1].")   
    W = np.eye(len(vi))  # Assume W = identification 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 a neighborhood 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 subsequent step is to compute the world value matrix dER(i,j). This matrix accumulates the minimal alignment value by contemplating three doable 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 general distance between two sequences of consuming events, we construct:

A native distance matrix deo crammed utilizing local_distance.

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

These instantly 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 (listing): First sequence of occasions (time, vitamins).
        ER2 (listing): 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)

    # # International value 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 value

ERA = listing(prepared_data['skipper_1'].gadgets())
ERB = listing(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 find out how to calculate the gap between two people’ consuming patterns utilizing MDTW, the subsequent 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 particular person i and particular person j.

That is applied within the perform beneath:

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 particular person.
        
    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 = listing(records1.gadgets())
                ER2 = listing(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 space 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, listing(prepared_data.keys()), save_path='distance_matrix.png')

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

This heatmap gives 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 evening eater have fewer, bigger meals concentrated later within the day, whereas the snacker distributes smaller meals extra evenly throughout the whole 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 real looking dietary behaviors. Since entry to large-scale dietary consumption datasets will be restricted or topic to utilization restrictions, I generated artificial consuming occasion information that simulate numerous every 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 individuals.
    Args:
        num_people (int): Variety of individuals to generate knowledge for.
        min_meals (int): Minimal variety of meals per particular person.
        max_meals (int): Most variety of meals per particular person.
        min_calories (int): Minimal energy per meal.
        max_calories (int): Most energy per meal.
    Returns:
        listing: Record of dictionaries containing artificial knowledge for every particular person.
    """
    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.kind(np.random.alternative(vary(24), num_meals, change=False))  # random occasions sorted

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

        person_record = {
            'person_id': f'person_{person_id}',
            'information': [
                {'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 = {particular person['person_id']: prepare_person(particular person) for particular person 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 standard strategies: the Elbow Methodology and the Silhouette Rating.

• The Elbow Methodology analyzes the clustering value (inertia) because the variety of clusters will increase. As proven within the plot, the price 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 value 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 = listing(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)', coloration=color1)
ax1.plot(ks, prices, marker='o', coloration=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:purple'
ax2.set_ylabel('Silhouette Rating', coloration=color2)
ax2.plot(ks, silhouette_scores, marker='s', coloration=color2, label='Silhouette Rating')
ax2.tick_params(axis='y', labelcolor=color2)

# Optionally available: 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), coloration='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()

Deciphering 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 certainly one of these clusters utilizing the Ok-Medoids mannequin. Now, we have to perceive what characterizes every cluster.

To take action, I adopted the method steered 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 every day consumption it represented. This gives perception into when individuals devour essentially the most energy and how a lot they devour 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 Day by day Consumption (largest meal)")
plt.grid(True)
plt.tight_layout()
plt.present()

Whereas the scatter plot gives a broad overview, a extra detailed understanding of every cluster’s consuming habits will be gained by inspecting their joint distributions.
By plotting the joint histogram of the hour and fraction of every day consumption for the biggest meal, we are able to determine attribute patterns, utilizing the code beneath:

# 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",
        type="scatter",
        top=6,
        coloration=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 Day by day Consumption (largest meal)", fontsize=12)
    g.fig.tight_layout()
    g.fig.subplots_adjust(prime=0.95)  # regulate title spacing
    plt.present()

To grasp how people had been distributed throughout clusters, I visualized the variety of individuals assigned to every cluster. The bar plot beneath exhibits 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 biggest consuming events throughout each the 24-hour day and the fraction of every 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 every day consumption, reflecting frequent however smaller meals all through the day. That is the cluster that most definitely represents “regular eaters” — those that devour 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 devour nearly their total every day caloric consumption (near 100%) in a single meal, predominantly throughout the early hours of the day (midnight to midday).

Cluster 3 (Late Night time Heavy Eaters) is characterised by people who devour almost all of their every day energy in a single meal throughout the late night or evening hours (between 6 PM and midnight). Like Cluster 2, this group displays a unimodal consuming sample with a very excessive fractional consumption (~1.0), indicating that almost all members eat as soon as per day, however not like Cluster 2, their consuming window is considerably delayed.

CONCLUSION

On this undertaking, I explored how Modified Dynamic Time Warping (MDTW) might help uncover temporal dietary patterns — focusing not simply on what we eat, however when and how a lot. Utilizing artificial knowledge to simulate real looking consuming behaviors, I demonstrated how MDTW can cluster people into distinct profiles like irregular or versatile eaters, frequent gentle eaters, early heavy eaters and later evening 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. For the reason that dataset was synthetically generated and clustering was primarily based on a single initialization, there are a number of caveats price 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, notably Cluster 0 and Cluster 1, making it more durable to differentiate between really completely different behaviors.
• With out labeled knowledge or anticipated floor fact, evaluating cluster high quality is troublesome. A possible enchancment could be to inject recognized patterns into the dataset to check whether or not the clustering algorithm can reliably recuperate them.

Regardless of these limitations, this work exhibits how a nuanced distance metric — designed for irregular, real-life patterns — can floor insights conventional instruments could overlook. The methodology will be prolonged to customized 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 undertaking — 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 study from others.

If you happen to discovered this convenient, have concepts for enhancements, or wish to collaborate, be happy to open a difficulty or ship a Pull Request on GitHub. Contributions are greater than welcome!

Thanks a lot for studying all the best way to the tip — it actually means rather a lot.

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 International Convention on Sign and Info Processing (GlobalSIP). IEEE, 2017.