Task-Specific Encoders, Decoders & Adapters

• Task-specific encoders, decoders, and adapters are modules that transform raw time-series data and latent representations to meet specialized forecasting, classification, or operational requirements.
• Decoders control output types such as point, quantile, parametric, or trajectory, directly influencing forecast structure and model utility in operational tasks.
• Adapters provide parameter-efficient tuning by integrating domain-specific biases and external covariates, enabling rapid adaptation to varying operational scenarios.

Time-series foundation models (TSFMs) are typically built as large, universal sequence models—most commonly transformer-based—pretrained on massive heterogeneous collections of time-series data. However, the effective application of TSFMs to specific forecasting, classification, or operational requirements often necessitates specialized architectural modules: task-specific encoders, decoders, and adapters. These components are functionally distinct from generic foundation model backbones, introducing targeted inductive biases, data interface mechanisms, and forms of representation adaptation that are critical for aligning model outputs to operational demands and domain-specific structures.

1. Definitions and Scope

Task-specific encoders, decoders, and adapters in TSFMs are modules or mechanisms that either preprocess, postprocess, or mediate information flow between the pre-trained backbone and downstream tasks:

• Encoders: Transform raw or preprocessed time-series data (potentially multivariate, irregular, or multi-modal) into latent representations suitable for the backbone or for direct consumption by a downstream head.
• Decoders: Map latent backbone representations into task-specific outputs: forecasts, probability distributions, classifications, or scenario ensembles. They control the form (point, quantile, parametric, trajectory) and structure (joint/marginal, multi-horizon, multi-scale) of forecasts (Perez-Diaz et al., 22 Oct 2025).
• Adapters: Lightweight, often learnable modules that inject task- or domain-specific priors, enable efficient parameter adaptation, or fuse external contextual information (exogenous covariates, additional modalities) with frozen foundation model representations (Qin et al., 14 Oct 2025, Park et al., 31 May 2025, Qiao et al., 17 Jun 2025).

This separation facilitates both rapid zero-shot deployment and fine-grained adaptation to operational constraints or evaluation metrics.

2. Task-Specific Forecast Typing and Decoders

TSFM decoders fundamentally determine the available operational utility by controlling the forecast type, which is not merely a matter of “head” architecture but imposes strict limits on the tasks a model can support. The taxonomy (Perez-Diaz et al., 22 Oct 2025):

Forecast Type	Output Structure	Typical Decoder Architecture
Point	$\hat{y}_{T+h}$	Linear regression head, MSE loss
Quantile	$Q_\tau(Y_{T+h}|X_{1:T})$	Multiple outputs per quantile, pinball loss
Parametric	$p(y;\theta_{T+h})$ (e.g., Gaussian)	Distribution parameter head, likelihood loss
Trajectory/Ensemble	$\{Y^{(m)}_{T+1:T+H}\}_{m=1}^M$	Autoregressive/stochastic sampling, diffusion or reparameterization decoders

• Trajectory/ensemble decoders are uniquely capable of supporting path-dependent tasks such as scenario generation, event probability computation, and window aggregate risk estimation, as only they preserve temporal dependence (Perez-Diaz et al., 22 Oct 2025).
• Marginal/parametric decoders (e.g., per-step Gaussian outputs) cannot answer questions about joint events or path-dependent statistics without external imputation of dependencies, typically via copula or conformal methods.
• Quantile decoders support interval estimation but do not provide information about pathwise excursions or aggregate distributions unless paired with additional dependence modeling.
• **Adapters here are critical for enabling forms of forecast type conversion (e.g., copula-based sampling to recover joint paths from marginals), but with the theoretical limitation that marginals alone cannot uniquely determine any joint law in the absence of further assumptions (Perez-Diaz et al., 22 Oct 2025).

3. Encoder and Tokenization Design for Task Alignment

The encoder and its associated tokenization/embedding strategy embed substantial inductive bias into the task interface (Yu et al., 22 Oct 2025, Feng et al., 30 Sep 2025):

• Patch Size & Adaptive Tokenization: Fixed patch size imposes a low-frequency/temporal smoothing bias, potentially missing high-frequency or local patterns necessary for shape-preserving or anomaly-centric tasks (Yu et al., 22 Oct 2025). Adaptive tokenizers such as Mixture-of-Size Dynamic Patching (Kairos) select variable granularity in response to local information density, improving performance on tasks with heterogeneous temporal structure (Feng et al., 30 Sep 2025).
• Covariate Handling & Modal Fusion Adapters: Standard TSFMs are typically pretrained on univariate inputs, with naive stacking or concatenation failing to capture heterogeneous inter-variable dependencies. Adapter frameworks such as CoRA interpose a covariate-aware alignment and fusion layer—often involving Granger-causality–based weighting—for principled injection of exogenous multivariate, textual, or visual covariates at the decoder (Qin et al., 14 Oct 2025).
• Positional/Temporal Encoding: Task requirements around irregular sampling, periodicity, or trend preservation motivate instance-adaptive encodings (e.g., IARoPE in Kairos), which tailor the Fourier domain of positional embeddings to the spectral structure of the input, improving generalization in diverse periodic or nonstationary regimes (Feng et al., 30 Sep 2025).

4. Adapters: Mechanisms and Parameter-Efficiency

Parameter-efficient adaptation is critical for deploying TSFMs in low-data or rapidly changing environments:

• Low-Rank Adaptation (LoRA) and other adapter modules enable high-accuracy fine-tuning with dramatically fewer trainable parameters than full backbone retraining, achieving equivalent or superior performance at a fraction of the computational cost (Park et al., 31 May 2025, Qiao et al., 17 Jun 2025). For instance, LoRA can achieve nearly identical MASE/RMSSE/MSIS/wQL as full fine-tuning on building energy forecasting, with less than 1% of the backbone parameters updated (Park et al., 31 May 2025).
• Multi-Scale Adapters (MSFT) exploit the inherent multi-scale nature of pretrained TSFMs by freezing pretrained backbone weights and injecting per-scale adapters, preserving generalization across temporal resolutions and preventing overfitting to the single scale of the fine-tuning data (Qiao et al., 17 Jun 2025).
• Covariate-Injection Adapters (CoRA) use frozen backbones, training only lightweight gating and alignment modules (e.g., Granger Causality Embedding) and adaptive layer normalization to inject external information, thus providing superior robustness and avoiding catastrophic forgetting (Qin et al., 14 Oct 2025).

These modular mechanisms enable rapid adaptation to new tasks or operational scenarios without degradation of the pre-trained representational base.

5. Role in Operational and Evaluation Frameworks

The choice and structuring of task-specific encoders, decoders, and adapters is not merely an implementation detail but is tightly coupled to what can be evaluated and deployed in practice (Perez-Diaz et al., 22 Oct 2025):

• Task alignment: Different operational objectives—prediction intervals, pathwise event detection, risk measurement, scenario generation—map directly to the needed forecast type and associated decoder or adaptation mechanism. For example, only trajectory-producing decoders can natively support scenario ranking or credible pathwise bands.
• Evaluation metrics: Proper scoring rules and calibration diagnostics depend on both the form of the forecast and the task at hand. Energy score, variogram score, CRPS, and Brier/IBS all presuppose access to appropriately-structured model outputs (i.e., sample ensembles, marginals, or probabilities) (Perez-Diaz et al., 22 Oct 2025).
• Limits of marginal adapters: There exist formal impossibility results—infinitely many joint distributions can share identical per-step marginals, leading to divergent event probabilities for path-dependent events. Thus, adapters that attempt to recover joint structure from only marginal information cannot guarantee pathwise calibration or event probability correctness (Perez-Diaz et al., 22 Oct 2025).
• Downstream adaptation: For uncertainty quantification, adapters such as conformalization modules can be applied to interval or point outputs from TSFMs, but optimality and calibration are only preserved when the underlying model structure supports the requirements of the target metric (e.g., CRPS, PIT histograms for marginals; energy score for scenarios) (Perez-Diaz et al., 22 Oct 2025).

6. Research Frontiers and Limitations

Several areas remain challenging in the deployment of task-specific encoders, decoders, and adapters in TSFMs:

• Spectral and Compositional Limitations: Current TSFM architectures demonstrate poor linear recoverability for spectral and time-warping features, and compositional interference in the representation of combined phenomena (e.g., trend + spectral, or abrupt changes over cycles), motivating architectural changes such as explicit Fourier layers, multi-scale attention, or nonlinear probing/adaptation (Pandey et al., 19 Nov 2025).
• Covariate and Multimodal Fusion: Extending adapters to enable dynamic, temporally-varying covariate integration, as well as more principled fusion of textual and visual modalities (e.g., via cross-modal transformers or temporal convolution), remains an open direction (Qin et al., 14 Oct 2025).
• Task-Agnostic Modularization: The design of decoders and adapters that support truly language- or prompt-driven selection of output type and structure (“head modularity”) is an emerging priority, as current designs still largely require task-specific training or architectural handcrafting (Mulayim et al., 12 Jun 2025).
• Fine-Grained Interpretability and Control: Enhancing steering and explanation (e.g., via latent-space vector arithmetic or explicit concept disentanglement) to support interactive or regulated deployment is beginning to be addressed in the TSFM literature (Wiliński et al., 19 Sep 2024), but robust methods for real-world settings are in early stages.

Emerging model designs and adaptation strategies for TSFMs are increasingly focused on maximizing task utility and alignment, minimizing compute and data requirements, and supporting robust, interpretable, and safe deployment across a breadth of downstream forecasting, detection, and decision tasks.