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docs: enhance foundation models guide with TSFM acronym and semantic intelligence
**Key Enhancements:**
- Introduce Time Series Foundation Models (TSFMs) acronym upfront
- Add broader bodies of knowledge references (NLP, CV foundation models)
- Emphasize "zero-shot revolution" and solution-oriented tone
- Drastically improve covariates vs examples distinction:
* TIME-DISTINGUISHED (covariates): temporal causality
* SHAPE TEMPLATES (examples): pattern recognition through cycles/seasonality
* Highlight TSFMs' semantic intelligence in distinguishing influence vs resemblance
- Add info panels for design decisions:
* Why GlobalForecastingModel (compatibility-first)
* fit() method purpose (validation, not training)
* Lazy loading pattern for true zero-shot
- Convert ASCII table to proper markdown
- Remove GenAI writing patterns ("it's not just X—it's Y")
- Remove references to moved architecture/roadmap docs
- Add experimental test confirming TimesFM is univariate-only
**References Added:**
- Foundation Models Survey (arxiv.org/abs/2108.07258)
- BERT paper (arxiv.org/abs/1810.04805)
- Vision foundation models (arxiv.org/abs/2010.11929)
- Chronos paper (arxiv.org/abs/2403.07815)
This documentation now accurately reflects TSFMs' robustness and their
sophisticated understanding of semantic differences in time series context.
🤖 Generated with [Claude Code](https://claude.com/claude-code)
Co-Authored-By: Claude <[email protected]>
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@@ -3,9 +3,15 @@ This document was written for darts version 0.30.0 and later.
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## What are Time Series Foundation Models?
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Time Series Foundation Models represent a paradigm shift in forecasting, similar to how large language models transformed natural language processing. These models are pre-trained on massive datasets containing over [100 billion time points](https://arxiv.org/abs/2310.10688) from diverse domains—energy consumption, financial markets, weather patterns, retail sales, and more. This extensive pre-training enables them to capture universal patterns in time series data: seasonality, trends, regime changes, and complex temporal dependencies.
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**Time Series Foundation Models (TSFMs)**represent one of the most exciting paradigm shifts in forecasting, paralleling how large language models like [GPT-4](https://openai.com/research/gpt-4) and [BERT](https://arxiv.org/abs/1810.04805) transformed natural language processing. These models bring the power of foundation model pre-training—pioneered in [NLP](https://arxiv.org/abs/2108.07258) and [computer vision](https://arxiv.org/abs/2010.11929)—to the time series domain.
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Unlike traditional Darts models that require training on your specific dataset, foundation models come ready to use out-of-the-box. They can generate forecasts immediately through **zero-shot inference**—no training required. Just like [GPT models](https://openai.com/research/gpt-4) can answer questions about topics they weren't explicitly trained on, time series foundation models can forecast patterns they've never seen before by leveraging their broad pre-training.
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The breakthrough lies in massive-scale pre-training: TSFMs learn from datasets containing over [100 billion time points](https://arxiv.org/abs/2310.10688) spanning diverse domains—energy consumption, financial markets, weather patterns, retail sales, web traffic, and more. This extensive exposure enables them to internalize **universal temporal patterns**: seasonality structures, trend behaviors, regime transitions, and complex dependencies that transcend any single domain.
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### The Zero-Shot Revolution
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Unlike traditional Darts models that require training on your specific dataset, **TSFMs come ready to use immediately**. They generate forecasts through **zero-shot inference**—no training required, no hyperparameter tuning, no dataset-specific configuration. Just like GPT can answer questions about topics it wasn't explicitly trained on, TSFMs can forecast patterns they've never seen before by recognizing analogous structures from their pre-training corpus.
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This is forecasting's "GPT moment"—the shift from domain-specific training to universal pattern recognition.
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The spectrum of foundation model usage includes:
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-**Zero-shot**: Direct prediction without any training
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This pre-training paradigm fundamentally changes the forecasting workflow. Instead of the traditional "fit-then-predict" approach, you can now "predict immediately" with competitive accuracy, making foundation models ideal for cold-start scenarios, rapid prototyping, and situations with limited historical data.
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## The Critical Distinction: Examples vs Covariates
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## The Semantic Intelligence: Understanding Examples vs Covariates
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Foundation models introduce a new concept that must not be confused with traditional covariates: **few-shot examples**. Understanding this distinction is crucial for using these models correctly.
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One of the most powerful capabilities of TSFMs is their ability to distinguish between fundamentally different types of information: **temporal causality** (covariates) versus **pattern templates** (examples). This semantic intelligence mirrors how humans naturally separate "what affects my target" from "what my target resembles."
Foundation models introduce **few-shot examples**—a concept semantically distinct from traditional covariates. Understanding this distinction unlocks the full power of these models.
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**Why mixing these concepts is semantically incorrect:**
|**Question:** "What affects my target?" |**Question:** "What does my target resemble?" |
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|**Semantic role:** Temporal causality<br>External influences at specific times |**Semantic role:** Pattern recognition<br>Behavioral templates from similar series (shape, cycles, seasonality) |
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|**Key dimension:** TIME<br>"Temperature on July 15 affects sales on July 15" - temporal alignment |**Key dimension:** SHAPE<br>"Store A's weekly pattern teaches retail seasonality" - shape learning |
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|**Structure:** TimeSeries objects (temperature, prices, holidays) |**Structure:** (context, future) pairs from analogous series |
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|**Time alignment:** MUST align with target |**Time alignment:** Independent (unaligned) |
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|**Persistence:** Used at EVERY time step throughout prediction horizon |**Persistence:** Ephemeral - used once to condition model, then discarded |
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|**Mechanism:** Feature extraction (traditional ML pattern) |**Mechanism:** In-context learning (foundation model pattern) |
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|**Example:** "Temperature influences ice cream sales moment-by-moment" |**Example:** "Store A's holiday spikes show how retail series behave—apply to B" |
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Covariates represent **causal or correlational relationships** that persist throughout the forecasting process. When you provide temperature as a covariate for ice cream sales, you're saying "temperature at time t affects sales at time t" consistently across all predictions. The model uses this relationship at every time step.
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### Why This Distinction Matters: TSFMs Understand Semantics
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Few-shot examples, however, are **demonstration pairs** used for in-context learning. They show the model "here's how similar time series behaved" without any temporal alignment to your target series. These examples are consumed once to condition the model's behavior, then discarded. They don't persist through the forecasting horizon.
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This distinction reveals a profound capability of foundation models: **they understand the semantic difference between influence and resemblance**.
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Attempting to use few-shot examples as covariates would be like using example sentences as grammar rules—they serve fundamentally different purposes. This distinction is why foundation models require their own API design, separate from traditional covariate handling.
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**Covariates encode temporal causality**: "Temperature at time t affects sales at time t." The model processes these relationships at every prediction step because the causal mechanism persists through time. This is traditional machine learning—feature engineering where you tell the model "pay attention to this external variable."
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## API Design Philosophy
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**Examples encode pattern templates**: "Here's how similar series behave—recognize these shapes, cycles, and seasonality structures." The model consumes these demonstrations once to understand "what kind of pattern am I forecasting," then applies that understanding. This is **in-context learning**—the foundation model innovation that enables zero-shot transfer.
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Foundation models break the fundamental contract of Darts' `ForecastingModel` base class: the requirement to call `fit()` before `predict()`. This isn't a limitation but a feature—zero-shot inference is the key innovation that makes these models immediately useful without training.
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The robustness comes from TSFMs' training: by seeing billions of time points across domains, they've learned to distinguish:
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-**When to look for external influences** (covariate-like patterns): "This series correlates with external factors"
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-**When to apply learned templates** (example-like patterns): "This series resembles retail/weather/financial patterns I've seen"
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TimesFM uses the standard Darts `GlobalForecastingModel` API. The `fit()` method is present for API compatibility:
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Attempting to use few-shot examples as covariates is like using example sentences as grammar rules—semantically incorrect. Examples teach "how to forecast this TYPE of series," while covariates provide "what influences THIS specific series." Foundation models' power lies in understanding both, separately.
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- For zero-shot inference, `fit()` simply validates inputs and loads the model
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- No training occurs - the pre-trained weights are used as-is
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- Some Darts utilities (like `historical_forecasts`) require calling `fit()`first
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## API Design Philosophy
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Foundation models break the fundamental contract of Darts' `ForecastingModel` base class: the requirement to call `fit()`before `predict()`. This isn't a limitation but a feature—zero-shot inference is the key innovation that makes these models immediately useful without training.
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This design ensures TimesFM works seamlessly with existing Darts workflows.
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### Design Decision: Using GlobalForecastingModel
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> **Why not create a separate `FoundationForecastingModel` base class?**
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>
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> TimesFM intentionally extends `GlobalForecastingModel` to integrate seamlessly with existing Darts workflows. While a custom base class exists (`FoundationForecastingModel`) for future models that may deviate further from Darts conventions, TimesFM's zero-shot paradigm actually *enhances* rather than replaces the standard API:
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>
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> - ✅ Works with `historical_forecasts()`, `backtest()`, and other Darts utilities
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> - ✅ Familiar API for existing Darts users
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> - ✅ Can be dropped into ensemble models
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> - ✅ Integrates with Darts metrics and evaluation frameworks
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>
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> This "compatibility-first" design means you can use TimesFM anywhere you'd use a traditional Darts model, but with the added superpower of zero-shot forecasting.
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### The fit() Method: Validation, Not Training
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> **Why does TimesFM have a `fit()` method if it doesn't train?**
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>
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> For API compatibility and input validation:
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> -**Validation**: Checks series are univariate, lengths are sufficient
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> -**Model loading**: Loads the pre-trained checkpoint (if not already loaded)
> -**Darts utilities**: Some tools (like `historical_forecasts`) require calling `fit()` before `predict()`
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> You can also use true zero-shot: call `predict()` directly without `fit()`, and the model will lazy-load automatically.
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### Lazy Loading Pattern
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> **How does zero-shot prediction without fit() work?**
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> TimesFM implements **lazy model loading**: when you call `predict()` without first calling `fit()`, the model automatically loads the pre-trained checkpoint on first use. This enables the most direct forecasting workflow:
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> ```python
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> model = TimesFMModel()
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> forecast = model.predict(n=12, series=my_series) # No fit() needed!
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>```
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> The model downloads once, then caches for subsequent predictions. This is the foundation model paradigm—immediate utility with no configuration.
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## Using TimesFM (PyTorch Version)
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@@ -166,12 +197,13 @@ Released in [v2.0.0](https://github.com/amazon-science/chronos-forecasting/relea
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