ML Interview Q Series: When would you use Sequential Split of data?

📚 Browse the full ML Interview series here.

Comprehensive Explanation

Sequential Split of data is typically employed in scenarios where the inherent order of the data is critical and must be preserved. This is especially important when the data has a temporal or sequential structure, such as in time-series forecasting or in any situation where observations collected at earlier time points could influence subsequent time points. In these contexts, randomly shuffling and splitting the data (as is commonly done with random train-test splits) would break the time dependence and lead to overly optimistic estimates of model performance.

When dealing with time-series or sequential data, preserving the temporal order in the split allows the model to be trained on past data and tested on future data, mirroring the real-world scenario of forecasting future events without knowledge of future observations during training.

Why Random Splitting Is Not Appropriate for Time-Dependent Data

In many machine learning tasks (especially those with data assumed to be i.i.d.), randomly splitting data into training and testing sets is acceptable because any data point is considered independent of the others. However, when dealing with sequential data, the chronological or structural ordering is part of the feature set. Random splitting would cause data “leakage” from the future into the training set. Consequently:

• The model might learn patterns that come from future data.

• The evaluation might not reflect how the model would perform in a genuine predictive scenario.

Typical Use Cases

Time-Series Forecasting If you have time-stamped data (e.g., daily stock prices, monthly sales figures, or sensor measurements collected over time), you train your model on an initial period and then test on the immediately following period. This approach simulates real-world prediction tasks.

Longitudinal/Sequential Observations In certain domains like medical research, you might have data from patients recorded over regular intervals. A sequential split allows for modeling patient state evolution over time without contaminating historical data with future clinical outcomes.

Incremental/Online Learning In streaming or incremental learning setups, data arrives continuously in time. Models need to be updated as new data is received, and sequential splitting is a straightforward way to simulate real-time model updates.

Practical Implementation Details

In many libraries like scikit-learn, you can manually implement a sequential split by choosing the cutoff index in your dataset that divides it into training and test segments based on chronological order. If you have a large temporal dataset, you could further adopt techniques like rolling-window validation where you move through the time series in blocks, always respecting chronological order.

Below is a brief example in Python for a simple sequential train-test split:

import numpy as np

# Suppose data is a numpy array with rows in chronological order
data = np.array([[i] for i in range(1, 11)])  # Just an example dataset

# Suppose we want the first 70% of data as train, remaining as test
train_size = int(len(data) * 0.7)
train_data = data[:train_size]
test_data = data[train_size:]

print("Train Data:", train_data.ravel())
print("Test Data:", test_data.ravel())

This ensures that any future observations are not present in the training set. In scikit-learn, you can also look at specialized classes like TimeSeriesSplit for cross-validation that accounts for temporal ordering.

Follow-Up Questions

What pitfalls might occur if you do a random shuffle instead of a sequential split?

One major pitfall is data leakage from the future into the model during training. If data from future time steps is accidentally placed in the training set, performance metrics during validation will not reflect real-world performance. In real-world forecasting tasks, the model cannot observe future data, so the metrics might become overly optimistic. Another risk is that the learned parameters might be overly tuned to patterns visible only when future data is partially in the training set.

How can you perform cross-validation with time-series data?

Unlike standard k-fold cross-validation, time-series cross-validation uses a series of sequential splits:

• In the first split, the model trains on the earliest chunk of data and tests on the chunk immediately after it.

• In each subsequent split, the training set grows forward in time, and the test set still comes from a future interval.

This is sometimes called “rolling cross-validation” or “walk-forward validation.” Each time slice respects chronological order, so there is no chance of leakage from future to past. There is a practical consideration in deciding how large each training window should be and whether the window is fixed-size (dropping oldest data points) or expanding over time.

How does concept drift impact your choice of sequential split?

Concept drift occurs when the statistical properties of the target variable or features change over time. In the presence of concept drift, you might prefer a rolling window approach that discards older data, so the model sees only recent data that reflects the most current patterns. A sequential split is a natural setup for detecting concept drift because you can observe performance decay as time moves on. If random splitting were done, it might obscure the fact that the data distribution is shifting over time.

Can sequential splits be combined with other approaches for better performance?

Yes. You can:

• Combine sequential splits with data augmentation. In time-series, you might engineer features like rolling averages, differences, or transformations that respect temporal ordering.

• Use specialized time-series cross-validation methods to get a better estimate of how well your model generalizes across different time windows.

• Apply hyperparameter tuning with a sequential approach, ensuring that any validation set you use is strictly from future data relative to the training set.

Why might you still do a quick random split check even if data is sequential?

In some rapid prototyping scenarios, data scientists might do a quick random split check to get a very rough sense of the model’s ability. However, they would never rely on such a check for final performance metrics or for decisions about model deployment in time-dependent tasks. The final, reliable evaluation still needs a sequential approach to match real-world conditions.

By preserving the chronological order in your splitting strategy, you ensure that your model’s performance estimate is more reliable for real-time or future predictions.

Below are additional follow-up questions

How do you handle the scenario where your time-series dataset has noticeable seasonal or cyclical patterns, and you still want to use a sequential split?

Seasonal and cyclical patterns mean that the statistical properties of your data repeat over certain intervals. If you rely purely on a straightforward sequential split, there is a risk that your training set might not contain the same seasonal phase as your test set, especially if your split happens at a point where a new seasonal cycle begins. This can lead to a mismatch between training and testing distributions, reducing the reliability of performance estimates.

One approach to mitigate this problem is to ensure that your training set captures multiple cycles or seasons, so the model sees the various recurring patterns. If your dataset is large enough, you might split in such a way that both training and test sets share at least one full seasonal cycle. An alternative is to perform rolling cross-validation, where each fold shifts the window over multiple seasons, making sure the model learns from a broader seasonal context. Edge cases arise when the seasonality is irregular, such as holidays or unexpected cyclical events; in these cases, you must carefully engineer features (like holiday indicators) and ensure the training set contains comparable periods to what appears in testing.

In a sequential split, how do you decide the exact point in time to separate training and testing sets, and what pitfalls might arise from poor selection of that point?

Choosing the split point often involves balancing your need to train the model on enough data against the desire to have enough future data for a robust test set. A common pitfall is choosing an arbitrary point that doesn’t reflect meaningful changes in the data distribution. For example, if a major event (economic downturn, new policy implementation, system upgrade) occurs just after your split, the training set may not prepare the model for drastic shifts in the test set distribution.

Another pitfall is that you might end up with too little future data for an adequately sized test set. This undermines the statistical reliability of your evaluation. Conversely, if you allocate too large a portion of the data for testing, your training set might become too small to capture the underlying dynamics. In real-world practice, domain knowledge and event analysis guide the selection of a split point, ensuring that the model’s training window reflects an appropriate historical period and the test window spans a realistic forecast horizon.

When using a sequential split with multiple training and test windows, what is the main difference between a rolling window approach and an expanding window approach, and when might you choose each?

In a rolling window approach, you fix the size of your training set and move forward through time in increments. For instance, you train on the first three months of data, then test on the next month, then shift the entire window forward by one month, discarding the oldest data point and adding a new one. This is advantageous when dealing with concept drift or when the distribution changes over time, because you focus on more recent patterns and disregard outdated trends.

In an expanding window approach, you continually add new data to your training set without discarding the oldest data points, so your training set grows over time. This is useful in stable or slower-changing environments because it lets the model accumulate a broader history, potentially recognizing long-term trends. However, in rapidly changing domains, an expanding window might lead to outdated patterns influencing the model’s decisions. A key pitfall with an expanding window is computational overhead, as each subsequent training iteration requires larger data volumes.

How can domain knowledge and business constraints affect the way you choose to perform your sequential splits?

Domain knowledge can inform which variables might shift in distribution over time, or which events might disrupt the data pattern. For instance, in finance, quarterly earnings announcements or macroeconomic reports may affect time splits. In retail, holiday seasons and promotional periods might cause significant deviations from typical behaviors. By factoring in domain-specific cycles, events, and constraints, you can ensure that the split reflects real-world conditions rather than arbitrary cut points.

Business constraints, such as how far in advance forecasts need to be made, can also shape your split. If the business requires monthly predictions, you might align your splits according to monthly boundaries. Alternatively, if the product release cycle is quarterly, you could mirror that structure so that training and test phases align with how forecasts or decisions are actually made in practice. Failure to incorporate domain and business context can result in a mismatch between the model’s performance estimates and real-world requirements.

What are some strategies to handle missing data in a time-series setting before doing a sequential split?

Sequential data with time dependencies can suffer more severely from missing values than i.i.d. data. Some strategies include forward filling or backward filling, where missing values are replaced with the last known or next known valid value. You could also use interpolation techniques, either linear or more advanced polynomial/spline methods, to estimate missing values based on surrounding time points.

A special consideration arises when large blocks of data are missing. Simply forward filling or interpolating might introduce heavy bias over those missing intervals. In such cases, you might segment the data into separate continuous chunks and perform separate sequential splits in each chunk if the missing gaps are irreconcilable or too large. Alternatively, if domain knowledge indicates that certain periods are entirely unrepresentative (e.g., sensor failure), discarding them might be more appropriate. The pitfall is that naive imputation of missing data can artificially preserve patterns that aren’t valid, resulting in misleading performance metrics.

How do you address the cold-start problem in sequential scenarios where there is no initial historical data for new entities or products?

The cold-start problem arises when you have minimal or no historical observations for a new entity, product, or user, making it hard to train and evaluate predictions. In a strictly chronological setup, one edge case is when a product is introduced late in the dataset, so it never appears in the training portion if you split too early. One strategy is to use data from “similar” entities to build an initial representation or baseline model. Another approach is to supplement the time-series model with external features or hierarchical modeling that borrows strength from other correlated products or categories.

You can also incorporate a “warm-up” phase for each new entity by allowing the model to learn from short bursts of data and then refine as more becomes available. The main pitfall is that if the training portion never contains examples of the new entity, the model might perform poorly in the test portion or real-world use. Hence, you must carefully design your training and validation sets to ensure new entities are not entirely absent from the training timeline if you need to predict them.

How do you guard against overfitting when using a sequential split, particularly in time-series with high autocorrelation?

When data points exhibit high autocorrelation, the model might memorize short-term fluctuations instead of learning more generalizable patterns. Overfitting in sequential settings can be subtle because the immediate future often shares strong similarity with the recent past. One way to mitigate this is to enforce a gap between the training and test sets. This gap, called a “hold-out period,” ensures that the model cannot simply rely on immediate-lag information in the test phase. Another approach is to use appropriate regularization techniques and incorporate domain knowledge about how far in time the dependencies realistically extend.

You can also use an out-of-time validation approach, where you test on data significantly later than your training set, to see how quickly model performance degrades. If your model quickly loses predictive power, it’s a sign you might be overfitting to very near-term patterns.

How do you handle real-time streaming data, where new data points constantly arrive after your training cut-off?

In real-time streaming scenarios, your model has to adapt on the fly to new incoming data. The conventional static sequential split becomes a sliding concept: you maintain a “live” model that was trained on historical data up to some point, and then periodically or continuously update it with the most recent observations. This process might involve online learning algorithms that update model parameters incrementally.

A pitfall here is deciding how frequently to update the model. If updates are too frequent, you could introduce instability because your model never settles. If updates are too infrequent, the model might fall behind new trends, especially if the data distribution changes rapidly. Another subtlety is evaluating performance in real time. You need a monitoring mechanism to detect when the performance has dropped enough to trigger a model retraining or adaptation phase.