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Time series analysis, a cornerstone of predictive analytics, has seen various advancements over the decades, with models evolving from traditional statistical methods to sophisticated deep learning approaches. Recently, the progress of foundation models, particularly in domains like natural language processing, has sparked interest in their application to time series forecasting. However, the excitement around foundation models brings with it critical questions: Can these models truly address the challenges inherent in time series forecasting? Or do the same limitations that have hindered deep learning in this domain still apply?
The relationship between deep learning and time series forecasting has been complex. The sequential nature of time series data, coupled with the need to preserve time-based dependencies, has made this field resistant to the advances that have driven other domains like image and text processing. Early attempts to apply deep learning to time series forecasting often fell short of expectations. Despite the ability of recurrent neural networks in handling sequential data, their performance in time series forecasting tasks has been inconsistent.
Zeng et al. (2022), in their paper "Are Transformers Effective for Time Series Forecasting?", question the effectiveness of Transformer models when applied to time series forecasting. The study argues that the order-independent nature of Transformers, which is beneficial in processing language and images, leads to a loss of critical time-based information in time series data. Surprisingly, the study shows that simple linear models often outperform Transformer-based models in long-term time series forecasting tasks, questioning the suitability of these advanced models for such applications.
Similarly, the paper "Why Do Tree-Based Models Still Outperform Deep Learning on Typical Tabular Data?" by Kadra et al. (2021) highlights the continued success of tree-based models like XGBoost and Random Forests over deep learning models in domains where tabular data is common. This research underscores the challenges deep learning models face in capturing the complex interactions within tabular data, a challenge that is also relevant to time series data, given its structured and sequential nature..
The application of foundation models to time series analysis represents a significant development in the field of machine learning. Despite inherent challenges, researchers are increasingly exploring the potential of these models, particularly in the domain of time series forecasting. These novel approaches aim to leverage the generalization capabilities of foundation models to address longstanding limitations in deep learning-based time series forecasting.
A comprehensive survey by Zhang et al. (2024) titled "Large Language Models for Time Series: A Survey" provides a systematic categorization of methods adapting Large Language Models (LLMs) for time series tasks. The survey delineates five primary approaches: prompting, quantization, aligning, vision as a bridge, and tool integration. While highlighting the potential of LLMs in handling complex time series tasks, the authors also emphasize critical challenges, including the need for advanced tokenization strategies and the integration of domain-specific knowledge. Complementing this work, Gruver (2023) in their paper "Large Language Models Are Zero-Shot Time Series Forecasters" demonstrate the efficacy of LLMs as zero-shot forecasters, a capability particularly valuable in scenarios characterized by data scarcity or rapid temporal evolution.
Chronos, developed by Ansari et al. (2024), represents one of the earliest attempts to adapt LLMs for time series data. The Chronos framework employs a novel tokenization method, scaling and quantizing time series data into a fixed vocabulary. This approach enables the training of transformer-based language models on these sequences. However, maintaining temporal continuity remains a significant challenge, crucial for effective time series forecasting.
Building upon these foundations, TimeGPT (Garza et al., 2023) emerged as a pioneering foundation model specifically designed for time series forecasting. Utilizing transformer-based architectures, TimeGPT is pre-trained on a diverse and extensive collection of time series data. This pre-training enables the model to generalize across various forecasting tasks with minimal fine-tuning, or even in a zero-shot manner. Empirical evaluations indicate that TimeGPT outperforms both traditional statistical methods and contemporary deep learning models across multiple benchmarking datasets.
The MOMENT family of models, introduced by Goswami et al. (2023), represents another significant contribution to the field. These open-source foundation models are designed for general-purpose time series analysis. Pre-trained on the "Time Series Pile," a large and diverse dataset, MOMENT demonstrates proficiency in tasks such as forecasting, classification, and anomaly detection across heterogeneous time series data.
Lag-Llama, developed by Rasul et al. (2024), presents a foundation model tailored specifically for univariate probabilistic time series forecasting. Employing a decoder-only transformer architecture, Lag-Llama incorporates lagged features as covariates, enabling more nuanced predictions by explicitly accounting for uncertainty—a critical aspect in probabilistic forecasting.
Parallel to these developments, State Space Models (SSMs) have emerged as promising tools for capturing complex temporal dynamics in time series forecasting. The S4 model (Structured State Space), introduced by Gu et al. (2022) "Efficiently Modeling Long Sequences with Structured State Spaces", demonstrated remarkable performance in handling long-range dependencies. Building on this work, the same research group developed the Mamba model, which presents a promising alternative to Transformers - the backbone of most foundation models. Explored in Gu and al. (2023) "Is Mamba Effective for Time Series Forecasting?", Mamba further advanced SSM capabilities. Its selective state space mechanism allows for efficient pattern capture in long sequences, potentially outperforming traditional Transformer-based models in various forecasting tasks. This development suggests that SSMs could play a crucial role in the next generation of foundation models for time series analysis.
Recent research has further expanded the application of LLMs to time series forecasting, yielding intriguing results. The study by Gruver et al. (2023), "Large Language Models Are Zero-Shot Time Series Forecasters", demonstrates that LLMs, primarily designed for text processing, can be effectively reprogrammed for time series forecasting. By recasting time series forecasting as a next-token prediction problem and encoding numerical data as text, the authors show that models such as GPT-3 and LLaMA-2 can match or surpass the performance of specialized time series models, particularly in zero-shot scenarios.
However, a critical examination by Tan et al. (2024) in "Are Language Models Actually Useful for Time Series Forecasting?" challenges the perceived advantages of LLMs in time series tasks. Through comprehensive ablation studies, the authors demonstrate that removing the LLM component from forecasting models often results in comparable or even improved performance. These findings raise important questions about the computational efficiency and practical utility of LLMs in time series analysis.
Offering a nuanced perspective, the TIME-LLM framework introduced by kin et al. (2023) in "TIME-LLM: Time Series Forecasting by Reprogramming Large Language Models" presents an alternative approach. This method adapts LLMs for time series forecasting by reprogramming input data into LLM-compatible formats without model fine-tuning. The authors' results indicate that LLMs, when appropriately adapted, can serve as effective forecasters, particularly in few-shot and zero-shot learning scenarios.
As we look forward to the inaugural NeurIPS 2024 workshop "Time Series in the Age of Large Models" and the future trajectory of time series analysis, it is evident that foundation models represent a significant advancement. These models offer a novel approach to addressing the challenges of time series forecasting by harnessing the capabilities of large-scale pretraining and transfer learning. However, the machine learning community must maintain a critical perspective and resist the assumption that foundation models are a universal solution to all complexities inherent in time series analysis.
Foundation models have undoubtedly brought us closer to a more unified and generalizable approach to time series analysis. Yet, they should not be viewed as the definitive answer to all challenges in this field. The issues highlighted in previous research remain pertinent, and the path to truly effective time series forecasting extends beyond merely scaling up existing models.
