Telematics — sensors transmitting real-time data from equipment, vehicles, or instrumented infrastructure — generates a particular kind of data: high-frequency time series, with sensor noise, gaps from signal loss, and patterns that shift with operational context. Turning that continuous stream into a reliable predictive model requires different technical decisions than a static tabular-data problem, and that’s where the choice between classical machine learning and deep learning stops being a stylistic preference and becomes an engineering decision.

Classical ML vs. Deep Learning for Time Series

A classical ML model — random forest, gradient boosting, even a regularized regression — needs someone to design the features: moving average over the last N readings, standard deviation in a window, rate of change, time of day. That feature engineering is where domain knowledge lives, and when the phenomenon is reasonably well understood, a classical model fed with good features is usually faster to train, more interpretable, and surprisingly competitive.

Deep learning — LSTM/GRU-style recurrent networks, or more recent attention-based architectures — learns directly from the raw sequence, without anyone hand-designing each feature. That’s valuable when the temporal pattern is complex or when there are too many input signals to manually engineer features for each one, but it has a real cost: it needs more data, more compute, and is notably harder to interpret when someone asks “why did the model predict this?”

An Example: From Raw Telemetry to a Risk Indicator

Consider a concrete case: sensors transmitting vibration, temperature, and pressure from operating equipment, with the goal of predicting a risk indicator before a failure materializes. A typical feature-engineering-to-prediction flow looks like this:

# Pseudo-code: sliding window -> features -> prediction
def build_features(window: list[dict]) -> dict:
    values = [r["vibration"] for r in window]
    return {
        "vib_mean": mean(values),
        "vib_std": stdev(values),
        "vib_trend": linear_slope(values),
        "temp_last": window[-1]["temperature"],
        "pressure_delta": window[-1]["pressure"] - window[0]["pressure"],
    }

def predict_risk(stream, model, window_size=60):
    buffer = deque(maxlen=window_size)
    for reading in stream:
        buffer.append(reading)
        if len(buffer) == window_size:
            features = build_features(list(buffer))
            risk_score = model.predict_proba(features)["high_risk"]
            if risk_score > ALERT_THRESHOLD:
                emit_alert(reading["sensor_id"], risk_score)

The part that almost never gets discussed enough is the alert threshold (ALERT_THRESHOLD): miscalibrating it in either direction has a real cost, either flooding whoever receives the alert with false positives or letting a real failure slip through. That’s the exact same threshold-calibration problem faced by any early-warning system, whether seismic or industrial: the predictive model is only half the system, the other half is deciding how sensitive the alert trigger should be.

When to Use Which

In my experience, the right question isn’t “ML or deep learning?” in the abstract, but: do I have enough labeled data to justify a neural network?, does the phenomenon have well-understood domain variables I can hand-design?, and do I need to be able to explain the prediction to someone who’s going to make an operational decision based on it? If the answers point to limited data and a need for interpretability, a well-built classical model wins almost every time. If there’s volume and the pattern is genuinely complex, deep learning starts earning its cost.

From Physical Sensors to Timely Decisions

This problem — turning noisy, continuous data streams into actionable indicators — is exactly the one I faced building spatiotemporal risk models for the public sector: the source changes (an industrial sensor, a GNSS station, a georeferenced incident report), but the engineering challenge is identical. Building that bridge well between the physical signal and the predictive model is, for me, the exact point where traditional engineering and data science stop being separate disciplines.