Zero-Shot Forecasting Capability

• Zero-shot forecasting capability is the ability of pre-trained time-series models to predict future values on unseen datasets without further fine-tuning.
• It employs advanced architectures such as transformers, state-space models, and retrieval-augmented designs to capture universal temporal patterns across diverse domains.
• Empirical evaluations show robust performance across sectors, reducing cold-start issues and enabling effective forecasting in data-limited environments.

Zero-shot forecasting capability denotes the ability of a time-series model—typically, a foundation or large pretrained model—to produce accurate forecasts for a previously unseen dataset, domain, or scenario without any gradient-based fine-tuning or dataset-specific retraining. The paradigm leverages the generic representations and temporal priors learned during large-scale, often multi-domain pretraining to enable immediate out-of-the-box generalization, which is especially valuable in sectors characterized by distribution shifts, limited local data, or frequent cold-start settings. This article surveys the technical frameworks, pretraining methodologies, model architectures, transfer mechanisms, evaluation protocols, and empirical results associated with state-of-the-art zero-shot forecasting systems.

1. Formal Definition and Core Principles

Zero-shot forecasting is defined by the mapping

$\hat y_{T+1:T+H} = f_\theta(X_{1:T})$

where $f_\theta$ is a pre-trained predictor whose parameters $\theta$ are fixed at inference; no further task-specific updates are allowed (Jetwiriyanon et al., 30 May 2025, Li et al., 24 Feb 2025). The model must generalize to target domains that may differ in granularity, seasonality, or statistical properties. Typical inputs include univariate or multivariate sequences, often accompanied by metadata, spatial-temporal indices, or auxiliary covariates.

The capability to perform zero-shot forecasting is attributable to extensive pretraining on broad, diverse time-series corpora (Das et al., 2023, Auer et al., 29 May 2025, Feng et al., 12 Feb 2024), the adoption of flexible architectures (e.g., transformers, state-space models, retrieval-augmented designs), and techniques for bridging the gap between training and deployment distributions.

2. Pretraining Strategies and Model Architectures

Zero-shot forecasting relies on pretraining strategies that internalize wide-ranging temporal patterns—trend, seasonality, regime shifts, noise—across domains. Architectures used include:

• Decoder-only transformers (e.g., TimesFM, Chronos, Sundial): Trained with autoregressive masking, using patch-based or quantile tokenization, and instance normalization for scale invariance. Performance is competitive with supervised baselines in zero-shot scenarios (Das et al., 2023, Jetwiriyanon et al., 30 May 2025, Zhang et al., 25 Nov 2025).
• Encoder-only transformers with multi-stage attention (e.g., GTT): Channel- and temporal-wise attention is used for scalable zero-shot multivariate forecasting (Feng et al., 12 Feb 2024).
• State-space models (e.g., Mamba4Cast): Trained exclusively on synthetic priors to yield highly efficient linear-time inference and robust generalization (Bhethanabhotla et al., 12 Oct 2024).
• Residual meta-learning structures (e.g., residual N-BEATS frameworks): Formulated as task-adaptive meta-learners, providing dynamic adaptation at inference (Oreshkin et al., 2020, Bhattacharya et al., 19 Dec 2024).
• Retrieval-augmented generators (e.g., TimeRAF, TS-RAG): Integrate external knowledge bases or historical repositories at inference via embedding similarity and learned mixing, providing flexible nonparametric context adaptation (Zhang et al., 30 Dec 2024, Ning et al., 6 Mar 2025, Deznabi et al., 19 Oct 2025).
• LLM-based prompt forecasters (e.g., LSTPrompt, TSLLM): Use advanced prompt engineering to map time-series tasks to natural LLMs’ in-context learning, sometimes with decomposition or multi-task prompts for better transfer (Liu et al., 25 Feb 2024, Li et al., 24 Feb 2025).

Training objectives include mean squared error (MSE), mean absolute error (MAE), quantile loss for probabilistic forecasting, and regularization or alignment losses for integrating retrieval or cross-domain signals. Models may train on diverse mixtures of synthetic and real-time series, often incorporating domain-invariant normalization and masking strategies.

3. Transfer Mechanisms and Adaptation without Fine-tuning

Zero-shot transfer is operationalized with several adaptation techniques:

• Prompt and semantic mapping: Carefully engineered time-series prompts, sometimes decomposed into trend/seasonal/residual or short/long-term blocks, bridge statistical series to LLM embedding spaces. Cosine similarity alignment and multi-task learning objectives reinforce universal representations (Li et al., 24 Feb 2025, Liu et al., 25 Feb 2024).
• Instance-wise normalization and decomposition: Per-window normalization, time-series decomposition (e.g., STL decompositions, wavelets), and patching facilitate transfer by removing scale and aligning context structures (Das et al., 2023, Li et al., 24 Feb 2025).
• Retrieval-augmented methods: External memory is harnessed by retrieving semantically similar time-series segments and adaptively fusing them, both for "resolution-aware" spatiotemporal transfer and for general nonparametric matching (Deznabi et al., 19 Oct 2025, Zhang et al., 30 Dec 2024, Ning et al., 6 Mar 2025).
• Synthetic prior coverage: Models such as ForecastPFN and Mamba4Cast train exclusively on synthetic generative models parameterized to densely cover possible trends, seasonality types, and noise processes; the PFN objective matches the Bayesian predictive distribution under these priors (Dooley et al., 2023, Bhethanabhotla et al., 12 Oct 2024, Nochumsohn et al., 24 Nov 2024).
• Covariate-aware adaptation: COSMIC extends transformer encoder-decoder models to support exogenous variables, using in-context covariate augmentation—enabling models to leverage auxiliary signals in zero-shot settings (Auer et al., 3 Jun 2025).

Table 1 summarizes key model classes and their transfer mechanisms:

Model/Class	Transfer Mechanism	Domain Adaptation
TimesFM/Chronos	Patch/token normalization	Diverse pretrain
GTT	Next-curve prediction + RevIN	Channel attention
Retrieval-aug. (TimeRAF/TS-RAG)	KB retrieval + mixing	Adaptive memory
TSLLM	Semantic prompt tuning	Multi-task alignment
ForecastPFN/Mamba4Cast	Synthetic prior covering	Bayesian/PFN
COSMIC	Informative covariate synth.	Covariate fusion

4. Quantitative Evaluation and Empirical Performance

Zero-shot forecasting claims are substantiated via standardized protocols:

• Holdout evaluation: Pretrained models are evaluated without tuning on benchmarks not seen during training (e.g., ETTm/h, Weather, Electricity, Exchange, GiftEval, Chronos-ZS, microclimate, mortality, macroeconomics) (Auer et al., 29 May 2025, Jetwiriyanon et al., 30 May 2025, Liu et al., 25 Feb 2024, Petnehazi et al., 17 May 2025, Deznabi et al., 19 Oct 2025).
• Metrics: MSE, MAE, sMAPE, MASE, CRPS, quantile loss, and probabilistic coverage metrics are standard. For chaotic systems, valid prediction time (VPT) and geometric properties of attractors are measured (Zhang et al., 24 Sep 2024).
• Comparative baselines: Direct comparison to ARIMA, Prophet, classic neural nets (LSTM, DeepAR), supervised SOTA (NBEATS, PatchTST, Fedformer), and, for retrieval-based and ensemble methods, the best out-of-the-box TSFM for each configuration.

Reported results highlight that:

• TSLLM, LSTPrompt, retrieval-augmented, and meta-learning models outperform classic and supervised baselines in out-of-domain and cross-household settings (Li et al., 24 Feb 2025, Ning et al., 6 Mar 2025, Zhang et al., 30 Dec 2024, Auer et al., 29 May 2025, Nochumsohn et al., 24 Nov 2024).
• Transfer is robust across domains (energy, economic, traffic, weather, environmental, mortality, chaotic) when pretraining is sufficiently diverse (Das et al., 2023, Petnehazi et al., 17 May 2025, Zhang et al., 25 Nov 2025, Fan et al., 8 Sep 2025).
• Fine-tuning or domain adaptation can further improve long-term accuracy, but zero-shot is often competitive in short-/medium-term scenarios (Petnehazi et al., 17 May 2025, Jetwiriyanon et al., 30 May 2025).
• Ablations confirm the necessity of synthetic prior variety, multi-tasking, resolution awareness, channel normalization, and retrieval for optimal performance (Zhang et al., 30 Dec 2024, Bhethanabhotla et al., 12 Oct 2024, Deznabi et al., 19 Oct 2025).

5. Interpretability, Limitations, and Deployment Guidance

Zero-shot forecasters increasingly address interpretability and deployment constraints:

• Interpretability: Retrieval-based methods (e.g., TS-RAG, TimeRAF) provide explicit rationale by exposing retrieved exemplars and gating weights, enabling users to trace model reasoning to specific reference patterns (Ning et al., 6 Mar 2025, Zhang et al., 30 Dec 2024).
• Uncertainty quantification: Large TSFMs (e.g., Moirai, TimeGPT, Chronos) produce predictive intervals without calibration; intervals widen appropriately during shocks but may lag post-regime-shift (Jetwiriyanon et al., 30 May 2025).
• Limitations: Failure modes include prior mismatch, extreme non-stationarity, unseen frequencies/seasonalities, or exogenous covariates absent at pretraining (Nochumsohn et al., 24 Nov 2024, Bhethanabhotla et al., 12 Oct 2024, Auer et al., 3 Jun 2025). Model scaling is critical; larger models generalize better but at increased computational expense.
• Deployment: Zero-shot models are recommended for rapid prototyping, cold-start tasks, data-poor environments, and as baselines for monitoring. For high-stakes or long-horizon forecasts, lightweight bias correction or quick fine-tuning may be necessary (Jetwiriyanon et al., 30 May 2025, Petnehazi et al., 17 May 2025).

6. Future Directions and Open Challenges

Research directions and challenges include:

• Adaptive retrieval/fusion: Online learning of retrieval/fusion modules or reward-aligned retrievers for dynamic environments (Zhang et al., 30 Dec 2024, Ning et al., 6 Mar 2025, Deznabi et al., 19 Oct 2025).
• Handling exogenous/covariate input: Further model development for robust non-retraining usage of auxiliary information (covariates, spatial indices, event markers) (Auer et al., 3 Jun 2025, Bhattacharya et al., 19 Dec 2024).
• Synthetic data design: Frequency-driven synthetic pretraining (e.g., Freq-Synth, PFN style) can efficiently cover rare or out-of-domain seasonalities for improved zero-shot accuracy (Nochumsohn et al., 24 Nov 2024, Dooley et al., 2023, Bhethanabhotla et al., 12 Oct 2024).
• Model ensembling and model zoo approaches: Efficient task-model matching using unified embedding spaces (e.g., ZooCast) enables dynamic selection/ensembling of complementary TSFMs at inference (Shi et al., 4 Sep 2025).
• Scaling laws and data requirements: Empirical analyses confirm that increasing model size and pretrain data volume monotonically improves zero-shot accuracy, in line with scaling laws observed in NLP and vision (Feng et al., 12 Feb 2024, Das et al., 2023).
• Robustness to distribution shift: Transfer during regime changes or under adversarial domain shifts remains an open theoretical and practical question (Jetwiriyanon et al., 30 May 2025, Bhattacharya et al., 19 Dec 2024).

7. Broader Implications

Zero-shot forecasting marks a shift in time-series forecasting practice, offering plug-and-play, low-overhead forecasting tools that match or outperform bespoke solutions across a spectrum of domains, benchmarks, and perturbation types. The blend of deep pretrained architectures, rigorous prompt/retriever engineering, and thoughtful normalization and regularization strategies enables real generalization beyond narrow, domain-specific solutions. The continuing convergence of foundation model approaches in time series with the capabilities seen in NLP and vision highlights an emerging universal forecasting paradigm, with strong implications for data-poor scientific and industrial applications.

---

References:

• (Li et al., 24 Feb 2025) "Zero-shot Load Forecasting for Integrated Energy Systems: A LLM-based Framework with Multi-task Learning"
• (Deznabi et al., 19 Oct 2025) "Resolution-Aware Retrieval Augmented Zero-Shot Forecasting"
• (Bhethanabhotla et al., 12 Oct 2024) "Mamba4Cast: Efficient Zero-Shot Time Series Forecasting with State Space Models"
• (Auer et al., 29 May 2025) "TiRex: Zero-Shot Forecasting Across Long and Short Horizons with Enhanced In-Context Learning"
• (Ning et al., 6 Mar 2025) "TS-RAG: Retrieval-Augmented Generation based Time Series Foundation Models are Stronger Zero-Shot Forecaster"
• (Jetwiriyanon et al., 30 May 2025) "Generalisation Bounds of Zero-Shot Economic Forecasting using Time Series Foundation Models"
• (Liu et al., 25 Feb 2024) "LSTPrompt: LLMs as Zero-Shot Time Series Forecasters by Long-Short-Term Prompting"
• (Bhattacharya et al., 19 Dec 2024) "Zero Shot Time Series Forecasting Using Kolmogorov Arnold Networks"
• (Petnehazi et al., 17 May 2025) "Zero-Shot Forecasting Mortality Rates: A Global Study"
• (Zhang et al., 30 Dec 2024) "TimeRAF: Retrieval-Augmented Foundation model for Zero-shot Time Series Forecasting"
• (Feng et al., 12 Feb 2024) "Only the Curve Shape Matters: Training Foundation Models for Zero-Shot Multivariate Time Series Forecasting through Next Curve Shape Prediction"
• (Zhang et al., 25 Nov 2025) "Zero-Shot Transfer Capabilities of the Sundial Foundation Model for Leaf Area Index Forecasting"
• (Auer et al., 3 Jun 2025) "Zero-Shot Time Series Forecasting with Covariates via In-Context Learning"
• (Shi et al., 4 Sep 2025) "One-Embedding-Fits-All: Efficient Zero-Shot Time Series Forecasting by a Model Zoo"
• (Fan et al., 8 Sep 2025) "WindFM: An Open-Source Foundation Model for Zero-Shot Wind Power Forecasting"
• (Dooley et al., 2023) "ForecastPFN: Synthetically-Trained Zero-Shot Forecasting"
• (Zhang et al., 24 Sep 2024) "Zero-shot forecasting of chaotic systems"
• (Nochumsohn et al., 24 Nov 2024) "Beyond Data Scarcity: A Frequency-Driven Framework for Zero-Shot Forecasting"
• (Das et al., 2023) "A decoder-only foundation model for time-series forecasting"
• (Oreshkin et al., 2020) "Meta-learning framework with applications to zero-shot time-series forecasting"