시간 시계열 기반 모델 툴킷으로 혁신적인 파이프라인 구축

📝 Abstract

Foundation models (FMs) have opened new avenues for machine learning applications due to their ability to adapt to new and unseen tasks with minimal or no further training. Time-series foundation models (TSFMs)-FMs trained on time-series data-have shown strong performance on classification, regression, and imputation tasks. Recent pipelines combine TSFMs with task-specific encoders, decoders, and adapters to improve performance; however, assembling such pipelines typically requires ad hoc, model-specific implementations that hinder modularity and reproducibility. We introduce FMTK, an open-source, lightweight and extensible toolkit for constructing and fine-tuning TSFM pipelines via standardized backbone and component abstractions. FMTK enables flexible composition across models and tasks, achieving correctness and performance with an average of seven lines of code. https://github.com/umassos/FMTK Although existing time series libraries such as sktime [10] , Darts [7], tsai [12] , and GluonTS [1] support classical and deep learning pipelines, they do not address the emerging need for composable, FM-centric evaluation. To this end, we introduce FMTK: an open-source, lightweight, and extensible Time Series Foundation Model Toolkit for constructing, fine-tuning and benchmarking modular TSFM pipelines. † Author holds concurrent appointments as a Professor at UCLA, and as an Amazon Scholar. This paper describes work performed at UCLA and is not associated with Amazon. NeurIPS 2025 Workshop on Recent Advances in Time Series Foundation Models (BERT 2 S).

💡 Analysis

Foundation models (FMs) have opened new avenues for machine learning applications due to their ability to adapt to new and unseen tasks with minimal or no further training. Time-series foundation models (TSFMs)-FMs trained on time-series data-have shown strong performance on classification, regression, and imputation tasks. Recent pipelines combine TSFMs with task-specific encoders, decoders, and adapters to improve performance; however, assembling such pipelines typically requires ad hoc, model-specific implementations that hinder modularity and reproducibility. We introduce FMTK, an open-source, lightweight and extensible toolkit for constructing and fine-tuning TSFM pipelines via standardized backbone and component abstractions. FMTK enables flexible composition across models and tasks, achieving correctness and performance with an average of seven lines of code. https://github.com/umassos/FMTK Although existing time series libraries such as sktime [10] , Darts [7], tsai [12] , and GluonTS [1] support classical and deep learning pipelines, they do not address the emerging need for composable, FM-centric evaluation. To this end, we introduce FMTK: an open-source, lightweight, and extensible Time Series Foundation Model Toolkit for constructing, fine-tuning and benchmarking modular TSFM pipelines. † Author holds concurrent appointments as a Professor at UCLA, and as an Amazon Scholar. This paper describes work performed at UCLA and is not associated with Amazon. NeurIPS 2025 Workshop on Recent Advances in Time Series Foundation Models (BERT 2 S).

📄 Content

FMTK: A Modular Toolkit for Composable Time Series Foundation Model Pipelines Hetvi Shastri1 Pragya Sharma2 Walid A. Hanafy1 Mani Srivastava2† Prashant Shenoy1 1University of Massachusetts Amherst 2University of California Los Angeles Abstract Foundation models (FMs) have opened new avenues for machine learning applica- tions due to their ability to adapt to new and unseen tasks with minimal or no further training. Time-series foundation models (TSFMs)—FMs trained on time-series data—have shown strong performance on classification, regression, and imputation tasks. Recent pipelines combine TSFMs with task-specific encoders, decoders, and adapters to improve performance; however, assembling such pipelines typically requires ad hoc, model-specific implementations that hinder modularity and repro- ducibility. We introduce FMTK, an open-source, lightweight and extensible toolkit for constructing and fine-tuning TSFM pipelines via standardized backbone and component abstractions. FMTK enables flexible composition across models and tasks, achieving correctness and performance with an average of seven lines of code. https://github.com/umassos/FMTK 1 Introduction Time Series Foundation Models (TSFMs), such as MOMENT [6], Chronos [2], and TimesFM [4], have emerged as powerful pre-trained architectures for a variety of downstream tasks, including forecasting, classification, and imputation. While these models serve as fixed backbones trained on large-scale temporal data, effective task specialization often requires integrating additional com- ponents: input encoders to structure raw data, task-specific decoders to generate predictions, and increasingly, parameter-efficient adapters (e.g., LoRA [8]) to enable lightweight fine-tuning. This modular design space, though conceptually flexible, has resulted in a fragmented and ad hoc implementation landscape. For example, models such as PaPaGei [13] and Mantis [5] require the development of extensive custom pipelines for simple comparison with state-of-the-art models. Moreover, models like Moment [6] offer distinct modes for different tasks (e.g., forecasting versus classification), which restricts the reuse of the same powerful backbone for a diverse set of downstream tasks during runtime. As a result, three key challenges emerge. 1 First, the absence of a unifying abstraction across encoders, backbones, adapters, and decoders significantly increases the engineering burden and inhibits systematic exploration of architectural variants. 2 Second, the lack of modular encapsulation complicates the attribution. It becomes difficult to isolate and measure the contribution of individual components to the overall performance of the model. 3 Third, evaluation practices vary widely across studies. Minor differences in data pre-processing, training regimes, or decoder heads can lead to substantial discrepancies in reported results, thereby undermining reproducibility. Although existing time series libraries such as sktime [10], Darts [7], tsai [12], and GluonTS [1] support classical and deep learning pipelines, they do not address the emerging need for composable, FM-centric evaluation. To this end, we introduce FMTK: an open-source, lightweight, and extensible Time Series Foundation Model Toolkit for constructing, fine-tuning and benchmarking modular TSFM pipelines. †Author holds concurrent appointments as a Professor at UCLA, and as an Amazon Scholar. This paper describes work performed at UCLA and is not associated with Amazon. NeurIPS 2025 Workshop on Recent Advances in Time Series Foundation Models (BERT2S). arXiv:2512.01038v1 [cs.LG] 30 Nov 2025  Foundation  Model (FM) Encoders Input Output

• Adapters A1 A2 A3 P=Pipeline(backbone=FM) Decoders E3 E2 E1 D3 D2 D1 P.add_decoder(D2(…), load=True) P.add_encoder(E1(…), load=True) P.add_adapter(A1(), load=True) train,test=Dataloader(..),Dataloader(..) P.train(train,[‘adapter’, ’encoder’, ‘decoder’]) y_test,y_pred=P.predict(test) Figure 1: Modular abstraction of pipeline construction using FMTK: The framework allows instantiating pipelines by pairing an FM with interchangeable components. Users can dynamically select and load components, specify trainable parts (e.g., decoder), and benchmark pipelines in a unified interface. We illustrate two example configurations using the same FM: (top) encoder- decoder-adapter-tuned pipeline with E1, A1 and D2; (bottom) encoder-decoder tuned with E3 and D3. Contributions: Our contributions can be summarized as follows:

1. FMTK proposes a standardized API for TSFM pipelines that defines a common grammar for connecting FM backbones with external encoders, fine-tuning adapters and decoders.
2. FMTK provides reference implementation of commonly used configurations and supports multiple time series tasks under consistent evaluation settings.
3. By decoupling architectural components and enforcing standardized execution semantics, FMTK facilitates reproducible experimentation and controlled compari

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