PLUTUS: A Well Pre-trained Large Unified Transformer can Unveil Financial Time Series Regularities

• URL: http://arxiv.org/abs/2408.10111v2
• Date: Tue, 20 Aug 2024 02:59:16 GMT
• Title: PLUTUS: A Well Pre-trained Large Unified Transformer can Unveil Financial Time Series Regularities
• Authors: Yuanjian Xu, Anxian Liu, Jianing Hao, Zhenzhuo Li, Shichang Meng, Guang Zhang,
• Abstract summary: Financial time series modeling is crucial for understanding and predicting market behaviors. Traditional models struggle to capture complex patterns due to non-linearity, non-stationarity, and high noise levels. Inspired by the success of large language models in NLP, we introduce $textbfPLUTUS$, a $textbfP$re-trained $textbfL$arge. PLUTUS is the first open-source, large-scale, pre-trained financial time series model with over one billion parameters.
• Score: 0.848210898747543
• License: http://creativecommons.org/licenses/by/4.0/
• Abstract: Financial time series modeling is crucial for understanding and predicting market behaviors but faces challenges such as non-linearity, non-stationarity, and high noise levels. Traditional models struggle to capture complex patterns due to these issues, compounded by limitations in computational resources and model capacity. Inspired by the success of large language models in NLP, we introduce $\textbf{PLUTUS}$, a $\textbf{P}$re-trained $\textbf{L}$arge $\textbf{U}$nified $\textbf{T}$ransformer-based model that $\textbf{U}$nveils regularities in financial time $\textbf{S}$eries. PLUTUS uses an invertible embedding module with contrastive learning and autoencoder techniques to create an approximate one-to-one mapping between raw data and patch embeddings. TimeFormer, an attention based architecture, forms the core of PLUTUS, effectively modeling high-noise time series. We incorporate a novel attention mechanisms to capture features across both variable and temporal dimensions. PLUTUS is pre-trained on an unprecedented dataset of 100 billion observations, designed to thrive in noisy financial environments. To our knowledge, PLUTUS is the first open-source, large-scale, pre-trained financial time series model with over one billion parameters. It achieves state-of-the-art performance in various tasks, demonstrating strong transferability and establishing a robust foundational model for finance. Our research provides technical guidance for pre-training financial time series data, setting a new standard in the field.

Related papers

• Intention-Conditioned Flow Occupancy Models [69.79049994662591]
  Large-scale pre-training has fundamentally changed how machine learning research is done today.<n>Applying this same framework to reinforcement learning is appealing because it offers compelling avenues for addressing core challenges in RL.<n>Recent advances in generative AI have provided new tools for modeling highly complex distributions.
  arXiv  Detail & Related papers  (2025-06-10T15:27:46Z)
• Time to Embed: Unlocking Foundation Models for Time Series with Channel Descriptions [0.0]
  Traditional time series models are task-specific and often depend on dataset-specific training and extensive feature engineering.<n>We introduce $textbfCHARM$, a foundation embedding model for multivariate time series that learns shared, transferable, and domain-aware representations.<n>The model is trained using a Joint Embedding Predictive Architecture (JEPA), with novel augmentation schemes and a loss function designed to improve interpretability and training stability.
  arXiv  Detail & Related papers  (2025-05-20T15:58:54Z)
• DELPHYNE: A Pre-Trained Model for General and Financial Time Series [2.601248228220401]
  Time-series data is valuable in financial applications, where it helps in detecting patterns, understanding market behavior, and making informed decisions based on historical data.<n>Recent advances in language modeling have led to the rise of time-series pre-trained models that are trained on vast collections of datasets and applied to diverse tasks across financial domains.<n>However, existing time-series pre-trained models have not shown boosts in performance over simple finance benchmarks in both zero-shot and fine-tuning settings.
  arXiv  Detail & Related papers  (2025-05-12T16:53:29Z)
• LLM4FTS: Enhancing Large Language Models for Financial Time Series Prediction [0.0]
  Traditional machine learning models exhibit limitations in this forecasting task constrained by their restricted model capacity.<n>We propose $LLM4FTS$, a novel framework that enhances temporal sequence modeling through learnable patch segmentation and dynamic wavelet convolution modules.<n>Experiments on real-world financial datasets substantiate the framework's efficacy, demonstrating superior performance in capturing complex market patterns and achieving state-of-the-art results in stock return prediction.
  arXiv  Detail & Related papers  (2025-05-05T06:48:34Z)
• Sundial: A Family of Highly Capable Time Series Foundation Models [64.6322079384575]
  We introduce Sundial, a family of native, flexible, and scalable time series foundation models.<n>Our models are pre-trained without specifying any prior distribution and can generate multiple probable predictions.<n>Sundial achieves state-of-the-art results on both point and probabilistic forecasting benchmarks with a just-in-time inference speed.
  arXiv  Detail & Related papers  (2025-02-02T14:52:50Z)
• BreakGPT: Leveraging Large Language Models for Predicting Asset Price Surges [55.2480439325792]
  This paper introduces BreakGPT, a novel large language model (LLM) architecture adapted specifically for time series forecasting and the prediction of sharp upward movements in asset prices. We showcase BreakGPT as a promising solution for financial forecasting with minimal training and as a strong competitor for capturing both local and global temporal dependencies.
  arXiv  Detail & Related papers  (2024-11-09T05:40:32Z)
• Large Language Models for Financial Aid in Financial Time-series Forecasting [0.4218593777811082]
  Time series forecasting in financial aid is difficult due to limited historical datasets and high dimensional financial information. We use state-of-the-art time series models including pre-trained LLMs (GPT-2 as the backbone), transformers, and linear models to demonstrate their ability to outperform traditional approaches.
  arXiv  Detail & Related papers  (2024-10-24T12:41:47Z)
• Time-MoE: Billion-Scale Time Series Foundation Models with Mixture of Experts [25.503695417712997]
  Time-MoE is a scalable and unified architecture designed to pre-train larger, more capable forecasting foundation models. Time-MoE enhances computational efficiency by activating only a subset of networks for each prediction. For the first time, we scaled a time series foundation model up to 2.4 billion parameters, achieving significantly improved forecasting precision.
  arXiv  Detail & Related papers  (2024-09-24T12:42:18Z)
• Unified Training of Universal Time Series Forecasting Transformers [104.56318980466742]
  We present a Masked-based Universal Time Series Forecasting Transformer (Moirai) Moirai is trained on our newly introduced Large-scale Open Time Series Archive (LOTSA) featuring over 27B observations across nine domains. Moirai achieves competitive or superior performance as a zero-shot forecaster when compared to full-shot models.
  arXiv  Detail & Related papers  (2024-02-04T20:00:45Z)
• Timer: Generative Pre-trained Transformers Are Large Time Series Models [83.03091523806668]
  This paper aims at the early development of large time series models (LTSM) During pre-training, we curate large-scale datasets with up to 1 billion time points. To meet diverse application needs, we convert forecasting, imputation, and anomaly detection of time series into a unified generative task.
  arXiv  Detail & Related papers  (2024-02-04T06:55:55Z)
• Towards a Foundation Purchasing Model: Pretrained Generative Autoregression on Transaction Sequences [0.0]
  We present a generative pretraining method that can be used to obtain contextualised embeddings of financial transactions. We additionally perform large-scale pretraining of an embedding model using a corpus of data from 180 issuing banks containing 5.1 billion transactions.
  arXiv  Detail & Related papers  (2024-01-03T09:32:48Z)
• TFMQ-DM: Temporal Feature Maintenance Quantization for Diffusion Models [52.454274602380124]
  Diffusion models heavily depend on the time-step $t$ to achieve satisfactory multi-round denoising. We propose a Temporal Feature Maintenance Quantization (TFMQ) framework building upon a Temporal Information Block. Powered by the pioneering block design, we devise temporal information aware reconstruction (TIAR) and finite set calibration (FSC) to align the full-precision temporal features.
  arXiv  Detail & Related papers  (2023-11-27T12:59:52Z)
• Time-LLM: Time Series Forecasting by Reprogramming Large Language Models [110.20279343734548]
  Time series forecasting holds significant importance in many real-world dynamic systems. We present Time-LLM, a reprogramming framework to repurpose large language models for time series forecasting. Time-LLM is a powerful time series learner that outperforms state-of-the-art, specialized forecasting models.
  arXiv  Detail & Related papers  (2023-10-03T01:31:25Z)
• Generative Temporal Difference Learning for Infinite-Horizon Prediction [101.59882753763888]
  We introduce the $gamma$-model, a predictive model of environment dynamics with an infinite probabilistic horizon. We discuss how its training reflects an inescapable tradeoff between training-time and testing-time compounding errors.
  arXiv  Detail & Related papers  (2020-10-27T17:54:12Z)
• Modeling Financial Time Series using LSTM with Trainable Initial Hidden States [0.0]
  We introduce a novel approach to modeling financial time series using a deep learning model. We use a Long Short-Term Memory (LSTM) network equipped with the trainable initial hidden states.
  arXiv  Detail & Related papers  (2020-07-14T06:36:10Z)

This list is automatically generated from the titles and abstracts of the papers in this site.

This site does not guarantee the quality of this site (including all information) and is not responsible for any consequences.