Context Length in TSFMs: How Much History Do Foundation Models Need?

Every time series foundation model has a maximum context length: the number of historical time steps it can ingest before producing a forecast. Across the current generation of TSFMs, this number varies substantially. Granite TTM accepts up to roughly 4,096 time steps. Chronos-Bolt supports around 8,192. MOMENT handles up to 12,288. And at the upper end, TimesFM and Moirai both support context lengths reaching 16,384 time steps. The natural instinct is to assume that longer context is strictly better -- that feeding a model more history always improves accuracy. In practice, the relationship is far more nuanced.

What Context Length Means in Practice

Context length defines the model's receptive field over raw input data. A model with a 4,096-step context window receiving daily data can look back approximately 11 years. The same model on hourly data can see about 170 days. On minute-level data, the window shrinks to under three days. The practical significance of any context length number depends entirely on the sampling frequency and the temporal structure of the underlying process.

This is the first decision point: before comparing model context lengths, translate the number from abstract time steps into the physical time horizon that matters for your domain. A 4K context may be more than sufficient for one use case and completely inadequate for another.

When More Context Helps

Longer lookback windows provide the most benefit when the data contains long-range dependencies that shorter windows would truncate. The clearest case is strong annual seasonality in daily data. Capturing a full yearly cycle requires at least 365 points, and reliably learning that pattern -- distinguishing it from noise -- typically requires two or three full cycles, pushing the useful context into the 700-1,100 range. Models with only a few hundred steps of context will see fragments of annual patterns and may misinterpret them as trends.

Slow-moving structural trends also benefit from extended history. A gradual multi-year demand shift in retail data, or a secular trend in energy consumption driven by population growth, only becomes visible when the model can observe enough of the trajectory to separate it from cyclical fluctuation. Complex multi-season patterns -- daily, weekly, and annual cycles overlapping in electricity demand, for example -- similarly reward longer context because the model needs enough data to disentangle the superimposed periodicities.

When More Context Hurts

More history is not free, and in some scenarios it actively degrades forecast quality. The most common culprit is concept drift: when the data-generating process changes over time, old observations reflect dynamics that no longer apply. Feeding a demand forecasting model three years of pre-pandemic data alongside post-pandemic data forces it to reconcile two fundamentally different regimes, often producing a blurred compromise that matches neither.

High-frequency noisy data presents a similar problem. Tick-level financial data or raw IoT sensor streams contain substantial measurement noise. Extending the context window on such data adds noise faster than it adds signal, and the model's attention mechanism may waste capacity attending to irrelevant distant fluctuations. Experiments on datasets from the Monash Time Series Forecasting Repository and long-range benchmarks consistently show that on many standard evaluation sets, accuracy improvements plateau or diminish beyond approximately 2,000 time steps. The gains from 512 to 2,048 steps are often substantial; the gains from 2,048 to 8,192 are frequently marginal.

The Patching Multiplier

Raw context length numbers can be misleading because of patching, the dominant tokenization strategy in modern TSFMs. Patching groups consecutive time steps into a single token. A model that accepts 16,384 tokens with a patch size of 32 has an effective context of 524,288 raw time steps -- over half a million. TimesFM, for instance, uses 512 input patches with a patch size of 32, yielding its 16K effective lookback.

This compression is what makes long context tractable. Without patching, self-attention cost scales quadratically with sequence length, making a 16K raw-token context prohibitively expensive. With patching, the transformer operates over a much shorter token sequence while still covering a vast temporal span. The trade-off is resolution: each patch aggregates local dynamics into a single representation, and events that straddle patch boundaries may be harder for the model to detect.

Memory, Latency, and the Practical Trade-Off

Even with patching, longer context is not free computationally. Doubling the effective context roughly quadruples attention cost (before any linear attention or sparse attention optimizations). GPU memory consumption scales linearly with sequence length for KV caches in decoder models and quadratically for full attention matrices. For production forecast pipelines, this translates directly into higher inference latency and infrastructure cost.

The practical question is whether the marginal accuracy from additional context justifies the marginal cost. In most production settings, the answer involves profiling: run your model at several context lengths on a representative validation set and measure both accuracy and latency. The diminishing-returns curve is usually steep enough that a clear elbow emerges well below the model's maximum capacity.

Practical Guidance for Choosing Context Length

A reasonable heuristic is to set your context length to the longest meaningful cycle in your data plus a buffer of one to two additional cycles. For daily data with weekly seasonality, 100 to 200 points (roughly 14-28 weeks) is typically sufficient. For monthly data with annual patterns, 36 to 60 points (three to five years) captures the relevant structure. For sub-hourly data with daily periodicity, 200 to 500 points covers multiple daily cycles without flooding the model with noise.

These numbers should guide model selection. If your data's longest meaningful cycle fits within 4K steps, a compact model like Granite TTM offers faster inference without sacrificing relevant context. If you need to capture multi-year annual patterns in daily data, the 16K context of TimesFM or Moirai becomes a genuine advantage rather than a marketing number. The differences in context length across models are architectural commitments, not just configuration parameters, and they reflect different assumptions about what forecasting problems the model is designed to solve.

The TSFM.ai model catalog lists the context length for every indexed model, alongside other architectural details. Use it to match your data's temporal characteristics to the right model, and refer to our benchmarking guide to understand how context length interacts with evaluation methodology. The right amount of history is not the most history -- it is the history that contains the patterns your forecast depends on.