Skip to main content

Timestamps

Timestamps are everywhere in modern systems: when a user signed up, when a product was added, when an article was published, when an event occurred, when the last interaction happened. While often stored simply as dates and times, these temporal features contain rich information that, when properly engineered, significantly boosts the relevance of search and recommendation systems. Understanding time allows systems to grasp:

  • Freshness & Recency: Is this content new? Did this interaction happen recently?
  • Seasonality & Trends: Is this item popular during specific times of day, days of the week, or months of the year? Are there recurring patterns?
  • User Lifecycle & Tenure: How long has this user been active? How old is this account?
  • Event Timing: When did a specific action (like a purchase or click) happen relative to now or other events?
  • Content Decay: Has the relevance of this item faded over time?

Transforming raw timestamp data into meaningful signals, or features, that machine learning models can effectively leverage is a crucial aspect of feature engineering. Get it right, and you unlock powerful temporal personalization and ranking dynamics. Neglect it, and models miss critical context about when things happen. The standard path involves careful handling of time zones, extraction of components, and derivation of relative time features.

The Standard Approach: Building Your Own Temporal Feature Pipeline​

Leveraging timestamp data effectively requires cleaning, normalization, and transformation to extract signals about seasonality, recency, and duration. Doing this yourself typically involves several steps:

Step 1: Gathering and Normalization​

  • Collection: Aggregate timestamp data from databases (e.g., created_at, updated_at columns), event logs, APIs, and user profiles.
  • Time Zone Handling (CRITICAL): This is the most common pitfall. Timestamps must be normalized to a standard time zone, typically Coordinated Universal Time (UTC), before any feature extraction. Storing naive timestamps (without time zone information) or mixing time zones leads to incorrect calculations for features like hour-of-day or day boundaries. Consistent parsing of various input formats (e.g., ISO 8601) is also key.
  • Type Consistency: Ensure timestamps are stored and processed using appropriate datetime objects or standardized formats (like Unix timestamps).

The Challenge: Time zone errors are subtle and can silently break temporal features. Ensuring all source data is correctly interpreted and converted to UTC requires diligence and robust data pipelines.

Step 2: Extracting Cyclical / Seasonality Features​

These features capture patterns that repeat over time.

  • Minute of the Hour: (0-59) - Rarely used unless very fine-grained patterns are expected.
  • Hour of the Day: (0-23) - Captures daily patterns (e.g., morning commute, evening browsing). Crucial: Must be calculated AFTER UTC conversion.
  • Day of the Week: (0-6 or 1-7) - Captures weekly patterns (e.g., weekend vs. weekday behavior).
  • Day of the Month: (1-31) - Less common for general patterns, but can be relevant for specific billing cycles, etc.
  • Day of the Year: (1-366) - Captures annual seasonal patterns.
  • Week of the Year: (1-53) - Alternative way to capture position within the year.
  • Month: (1-12) - Captures monthly or annual seasonal patterns (e.g., holiday shopping).
  • Year: Captures long-term trends or year-specific effects.

Advanced Technique: For cyclical features like hour or month, consider cyclical encoding (e.g., using sin and cos transformations) so the model understands that hour 23 is close to hour 0, or December is close to January.

The Challenge: Requires careful implementation using datetime libraries. Choosing which components to extract depends on the expected patterns in your domain.

Step 3: Deriving Relative Time / Duration Features​

These features measure the time elapsed between two points, often relative to the current time (NOW()).

  • Time Since Event: Calculate the duration (in seconds, minutes, hours, days, months, years) between the timestamp and the time of the request/inference.
    • Examples: days_since_last_purchase, hours_since_signup, months_since_content_published.
  • Age: Similar to "time since," often used for static attributes like account_age.
  • Time Until Event: For future timestamps (e.g., sale start date), calculate time remaining.

The Challenge: Requires knowing the reference time (NOW()). This needs to be consistently applied during both training (using the event time of the training data) and inference (using the actual time of the request). Feature stores can help manage this consistency. Calculations need to handle time units correctly.

Step 4: Binning and Creating Categorical Flags​

Convert continuous time information or extracted components into simpler categories.

  • is_weekend: Boolean flag based on Day of the Week.
  • time_of_day: Categorical feature (e.g., 'Morning', 'Afternoon', 'Evening', 'Night') derived from Hour of the Day. Requires careful definition based on UTC or a consistently applied local time.
  • is_recent: Boolean flag indicating if the timestamp falls within a defined recent period (e.g., last 7 days, last 30 days) relative to NOW().
  • publication_year_bin: E.g., 'Last Year', '2-5 Years Ago', 'Older'.

The Challenge: Defining meaningful bins requires domain knowledge and experimentation. Definitions involving NOW() must be calculated dynamically at request time. Time zone awareness is critical for bins like time_of_day.

Step 5: Handling Nulls / Missing Timestamps​

  • Strategies:
    • Imputation: Fill with a specific placeholder date (e.g., Unix epoch start 1970-01-01, a very old date, or sometimes the mean/median if calculating durations first).
    • Flagging: Create a separate boolean feature is_timestamp_missing.
    • Model Handling: Some models can inherently handle missing values.
  • Context Matters: The best strategy depends on what the timestamp represents (e.g., a missing last_purchase_date might imply a new user).

The Challenge: Choosing an appropriate imputation value that doesn't mislead the model.

Step 6: Integration & Usage Context​

Timestamp features can be used at different stages:

  • At Retrieval Time (Filtering): Use date ranges or is_recent flags to initially filter candidates (e.g., only show news from the last 48 hours, only retrieve products added after a certain date).
  • At Scoring Time (Ranking): Feed extracted features (hour, day of week, time since, is_weekend) as inputs into the main ranking ML model. The model learns the predictive power of these temporal signals.
  • At Ordering Time (Post-Processing): Apply business rule boosts or re-ranking based on freshness after the main model scoring (e.g., boost items published today).

The Challenge: Requires coordinating feature availability and calculation logic across different system components (retrieval, ranking).

Streamlining Temporal Feature Engineering​

The DIY path for timestamp features requires meticulous handling of time zones, careful feature extraction logic, and consistent application relative to the request time. Platforms and tools aim to simplify this significantly.

How a Streamlined Approach Can Help:

  1. Automated Type Inference & Normalization: Automatically detect timestamp columns and enforce UTC normalization by default, preventing common time zone errors.
  2. Automatic Feature Extraction: Automatically derive common useful features like hour_of_day, day_of_week, and potentially time_since_now (calculated relative to request time) from identified timestamp columns.
  3. Native Integration: Seamlessly integrate these automatically generated temporal features alongside behavioral, text, image, and other numerical features within unified ranking models.
  4. Contextual NOW() Handling: Manage the calculation of recency/duration features relative to the actual time of inference automatically.
  5. Managed Infrastructure: Abstract away the need to build and maintain separate pipelines specifically for time-based feature generation and serving.
  6. Sensible Null Handling: Provide robust default strategies for handling missing timestamp values.

Leveraging Timestamps with Shaped​

Here's how you can use Shaped to streamline temporal feature engineering:

Goal: Automatically use publish dates and event times to improve recommendations.

1. Ensure Data is Available:
Assume item_metadata (with published_at) and user_events (with event_timestamp) are accessible.

2. Define Model Configuration (YAML):

temporal_model.yaml
model:
  name: temporal_recs_platform
connectors:
  - name: items
    type: database
    id: items_source
  - name: events
    type: event_stream
    id: events_source
fetch:
  items: |
    SELECT
      item_id, title, category,
      published_at  # <-- Shaped identifies this as a timestamp
    FROM items_source
  events: |
    SELECT
      user_id, item_id, event_type,
      event_timestamp  # <-- Shaped identifies this as a timestamp
    FROM events_source

3. Create the Model & Monitor Training:

shaped create-model --file temporal_model.yaml

# Monitor the model until it reaches the ACTIVE state
shaped view-model --model-name temporal_recs_platform

4. Use Shaped APIs:
Call Shaped's rank API to get recommendations. The relevance scores will automatically incorporate temporal features like freshness, user activity patterns, and content age.

from shaped import Shaped

# Initialize the Shaped client
shaped_client = Shaped()
response = shaped_client.rank(
    model_name='temporal_recs_platform',
    user_id='USER_1',
    limit=10
)
for item in response.metadata:
    print(f"- {item['title']} (Published At: {item['published_at']})")

Conclusion: Make Time Work For You, Minimize Temporal Pain​

Timestamps are a fundamental data type, but unlocking their full potential for relevance requires careful feature engineering beyond simply storing the date and time. Handling time zones correctly, extracting cyclical patterns, calculating relative durations, and using these features appropriately at retrieval, scoring, or ordering time are essential but complex tasks.

Streamlined platforms and MLOps tools can significantly reduce this burden by automating UTC normalization, common feature extraction (like hour, day of week, recency), and integration into models, all while handling the context of the request time dynamically. This allows teams to leverage the powerful signals hidden within timestamps—freshness, seasonality, user behavior timing—without getting bogged down in the intricate details of temporal calculations, leading to more timely and relevant user experiences.

Ready to streamline your feature engineering process?

Request a demo of Shaped today to see Shaped in action for your feature types. Or, start exploring immediately with our free trial sandbox.