arXiv:2609.15344v1 Announce Type: new
Abstract: We study the adaptation of pretrained language models to univariate time-series forecasting through a parameter-efficient transfer learning framework,...
By Tamanna Kumavat, Georg Brunner, Kyriakos Flouris
The paper introduces PRICE, a systematic framework for adapting Large Language Models to short‑term Bitcoin price forecasting. PRICE combines parameter‑efficient fine‑tuning with LoRA, recursive multi‑step inference, integer‑rounded numerical representation, Context‑Task‑Format prompting, and exact zero‑temperature decoding, all built on a 4‑bit quantized LLaMA‑3 8B model. Ablation studies and comparative evaluations show that each component improves accuracy and reliability, enabling PRICE to achieve the lowest forecasting errors among eight transformer‑based and time‑series foundation models.
By Maryam Fakhari, Mehran Safayani
arXiv:2606. 24950v1 Announce Type: new Abstract: Financial decision-making is contextual: forecasting prices, valuing companies, and assessing event exposure weigh price history, accounting fundamentals, macroeconomic regime, and contemporaneous text.
By Patara Trirat, Jin Myung Kwak, Jay Heo, Heejun Lee, Sung Ju Hwang
NeST is a framework that adapts large language models (LLMs) for continuous time‑series forecasting by creating neighborhood‑aware text prototypes and aligning them with temporal representations through a nearest‑neighbor contrastive objective. It retrieves the most relevant prototypes and uses them to conditionally modulate time‑series features, enabling more effective integration of textual and temporal information. Experiments show that NeST outperforms state‑of‑the‑art methods on eight benchmarks, reduces MSE by 1.2% for long‑term forecasting, improves zero‑shot forecasting by 4.9%, and boosts R² by 3.3% on a real‑world photovoltaic power forecasting task.
By Jayanie Bogahawatte, Sachith Seneviratne, Maneesha Perera, Saman Halgamuge
VertiFuseX is a hybrid LSTM architecture that fuses multi‑scale temporal representations at the penultimate layer, stacking features from LSTM, Bi‑LSTM, and St‑LSTM branches and a parallel DNN stream. On 15 years of global equity index data, it reduces MAPE by 30‑54% and improves MAE and RMSE by over 40% compared to LSTM baselines, outperforming seven state‑of‑the‑art models across 33 metric‑dataset comparisons. The model is lightweight (675k parameters, 2.6 MB footprint) with 1.5 ms/sample inference latency and demonstrates robust, interpretable forecasting with reduced drawdowns in algorithmic trading simulations.
By Aashish Bohra, Vivek Vijay
arXiv:2608. 04051v1 Announce Type: new Abstract: Real-world time series are often governed by recurring patterns, but their dominant periods may vary across datasets, forecasting settings, and individual input windows.
By Jung Min Choi, Vijaya Krishna yalavarthi, Lars Schmidt-Thieme