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Abstract:
This presentation discusses the emerging field of foundational models for time series forecasting. Despite initial technical difficulties and a condensed timeframe, the speaker explores the potential and challenges of using large, Transformer-based models trained on diverse time series datasets for prediction tasks, particularly aiming for zero-shot forecasting capabilities. The talk highlights the historical underperformance of deep learning in forecasting compared to traditional methods but notes recent successes in specific domains with large datasets. Key challenges addressed include the significant lack of large, diverse public time series data compared to the NLP domain, the absence of standardized evaluation datasets leading to benchmarking difficulties and contamination risks, and concerns regarding the use and implications of synthetic training data. The presentation briefly introduces specific model architectures like Kronos (repurposing language models via quantization) and TimesFM (using a custom patching approach) and their respective training strategies. While acknowledging the promise of zero-shot deployment and the potential of fine-tuning, the speaker concludes that the field is nascent, and reliable evaluation currently requires building custom benchmarks due to data and standardization limitations.
Foundational Models for Time Series Forecasting: Potential and Challenges
0:00:19 Introduction & Apology: Session begins, speaker (Luka) joins late due to time zone confusion and plans a speedrun presentation.
0:03:54 Core Question: The presentation explores whether foundational models (popularized recently) can be effectively used for time series forecasting in production, based on research with colleague Gabriel.
0:04:38 Transformers' Past: Historically, Transformer and deep learning models haven't outperformed traditional forecasting methods (like ARIMA) or even gradient-boosted trees in autoregressive setups.
0:05:11 Rationale for Foundational Models: Success was seen when large companies (e.g., Amazon, Zalando) trained large, specific models on vast internal datasets (e.g., for retail forecasting), even observing scaling laws. This led to the idea of training general models on diverse public/private time series data.
0:06:16 What are Foundational Models?: Defined as arbitrarily large, Transformer-based neural networks trained on any available time series data, aiming to learn universal patterns across domains, frequencies, and scales.
0:06:47 Key Capability - Zero-Shot Forecasting: These models aim to generate predictions for any time series without specific retraining, using the input data directly for inference.
0:07:13 Benefit of Zero-Shot: Allows for rapid deployment, especially useful when initial data is scarce (e.g., 10-60 data points). The model can provide immediate forecasts while more data is collected for benchmarking or fine-tuning. It's about accessibility and scalability, not necessarily optimal accuracy initially.
0:08:24 The Data Scarcity Problem: A major weakness is the limited availability and diversity of public time series datasets compared to NLP. Examples: Monash Repository (<1B observations), Lota (~27B), Time Series File (smaller). NLP datasets like Llama's training data reach trillions of points.
0:10:41 Evaluation Challenges: Lack of dedicated, standardized evaluation datasets makes robust benchmarking difficult and prone to contamination (models evaluated on data potentially seen during training by other models).
0:11:33 Use of Synthetic Data: Researchers often augment real data with synthetic data (e.g., combining simple processes, convex combinations) which improves performance, but raises questions about what the model is truly learning and its reliability, especially in critical domains (e.g., healthcare).
0:13:18 Model Examples: Several models exist (from public and private labs). Kronos (AWS) and TimesFM are highlighted. Mentions a DataDog model trained on large private, anonymized data.
0:14:33 Kronos Approach: A framework repurposing existing Transformers (GPT-2, T5). Uses quantization (mean scaling + binning) to convert continuous time series values into discrete tokens, suitable for language model architectures. Outputs probabilistic forecasts. Trained on 55 public datasets with specific splits for training/evaluation.
0:16:55 TimesFM Approach: Uses a custom architecture (like vision transformers) employing patching (non-overlapping input/output windows, e.g., 32 points in, 128 points out) directly on continuous data. Trained on ~100 billion data points, including Wikipedia traffic data. Views synthetic data as teaching the "grammar" of time series.
0:18:37 Fine-Tuning Potential: Foundational models can potentially be fine-tuned on smaller, specific datasets (e.g., using LoRA with Kronos via Hugging Face libraries), improving performance beyond zero-shot.
0:19:28 Current State & Conclusion: The field is promising but nascent. Public benchmarks are hard to trust due to potential data contamination. Building your own robust evaluation framework is currently mandatory for reliable assessment.
0:20:05 Acknowledgements: Thanks colleague Gabriel and invites further questions via Discord or LinkedIn.