Introduction: Why Predictive Caching Matters

Aviation connectivity has reached a crossroads. As in-flight entertainment (IFE) shifts from seatback to streaming, passengers expect fast, reliable access to movies, music, and apps—without buffering or dropouts. However, aircraft remain limited by high-latency satellite links and constrained bandwidth. Predictive caching, powered by AI and machine learning, is emerging as a critical solution—delivering the right content before it’s requested.

Why Static Rules No Longer Work

Legacy caching systems rely on static rules—like pushing the same “top 20 titles” to every aircraft. But air travel is anything but static. Routes, passenger demographics, and demand patterns change daily.

For example:

  • A flight to Tokyo may have more interest in Japanese cinema.

  • Weekday business travelers prefer productivity apps over movies.

  • School holidays shift demand toward children’s content.

To meet real-time needs, caching must evolve—from reactive to predictive.

ML Forecasting Models: LSTM, Prophet & Beyond

Modern machine learning models enable time-series forecasting of content demand:

  • LSTM (Long Short-Term Memory) networks excel at modeling sequential data and capturing temporal patterns in viewing habits.

  • Meta/Facebook Prophet is useful for forecasting based on seasonality and events—perfect for fluctuating passenger behavior across seasons, routes, or holidays.

  • Hybrid models can combine structured metadata (e.g., title popularity) with real-time data streams (e.g., device pings, watch duration) to refine predictions.

By learning what content is likely to be requested before the aircraft departs, systems can intelligently cache content that maximizes hit rates and minimizes unnecessary satellite usage.

Feeding the Models: What Data Drives Accuracy?

Prediction engines require diverse datasets, including:

  • Historical watch and download patterns by aircraft type, route, and time

  • Passenger demographics (age, language, loyalty tier, etc.)

  • Flight-specific data like departure and destination airports, flight time, and aircraft capacity

  • External events such as blockbuster releases or live sports matches

Using this data, models generate confidence-weighted demand forecasts—so that high-likelihood titles are prioritized for pre-positioning.

Edge Architecture: Cloud Training, Local Inference

A modern predictive caching architecture typically includes:

  1. Model Training in the Cloud

    • Aggregates cross-fleet data

    • Continuously retrains models to improve accuracy

  2. Lightweight Inference at the Edge

    • Aircraft receive pre-trained models

    • Onboard systems make real-time caching decisions as needed

  3. Content Sync via airport 5G or satellite backhaul

    • Scheduled updates based on predicted needs

    • Efficient use of low-cost ground connectivity where available

Siden Edge Architecture

Measurable ROI: What Airlines Gain

Implementing predictive caching delivers measurable benefits:

  • 80–90% cache hit ratio during peak demand

  • 50–70% reduction in satellite airtime costs

  • Higher passenger satisfaction (QoE) from instant-start playback

  • Lower storage waste from unused or irrelevant content

This shift also reduces operational overhead from manual content curation across aircraft.

How Siden Solves the Problem

Siden’s Predictive Edge Caching Platform is purpose-built to anticipate content demand in aviation:

  • AI-powered Intelligence Engine ingests route data, passenger profiles, title popularity, and real-time usage

  • Custom-trained ML models predict which titles each aircraft needs, per route and daypart

  • Smart sync orchestration chooses the best path (airport Wi-Fi, 5G, satellite) and time to update each aircraft’s cache

  • Local inference agents on aircraft refine predictions and adapt mid-flight if needed

Siden enables airlines to pre-position only the most relevant content—reducing costs and improving user experience.

Key Takeaways

As aircraft become flying data centers, caching must get smarter. With machine learning edge intelligence and accurate content demand forecasting, predictive caching transforms in-flight streaming. Airlines that adopt AI-first approaches like Siden’s can leap ahead in performance, cost, and passenger delight.

About Siden

Founded in 2018, Siden is at the forefront of revolutionizing connectivity in the aviation, maritime, and home broadband industries. Siden optimizes connectivity platforms through intelligent caching, enabling higher-quality content delivery while reducing network costs.