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arXiv research

A locally-built, LLM-digested index of recent arXiv papers in quant finance, geometry/topology, and statistical ML — keyword search served straight from SQLite on this machine.

168,695 papers · 148 categories

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11 results for “TimesFM”

Financial Fine-tuning a Large Time Series Modelq-fin.CP

Fine-tuning a time series model improves financial price prediction accuracy.

problem Improving accuracy in predicting financial market prices using large models.

method Continual pre-training of a time series foundation model on financial data to fine-tune its performance for price prediction.

result The fine-tuned model outperforms the baseline in various financial metrics.

time series forecasting financial modeling price prediction large models continual pre-training

2024-12-13abs ↗pdf ↗

Foundation Time-Series AI Model for Realized Volatility Forecastingq-fin.RM

Foundation models improve volatility forecasting in finance.

problem Improving volatility forecasting in financial markets.

method Evaluation of TimesFM model, incremental fine-tuning, comparison with econometric benchmarks.

result Incremental fine-tuning improves forecast accuracy and outperforms traditional models.

volatility forecasting foundation models TimesFM incremental learning financial risk management

2025-05-16abs ↗pdf ↗

When Directional Accuracy Lies: A Base-Rate-Honest Benchmark for LoRA-Adapted TimesFM on Equity Forecastingq-fin.ST

A study shows that a fine-tuned model's directional accuracy in financial forecasting is largely due to chance, not skill.

problem Misleading directional accuracy in financial forecasting models.

method A reproducible, frozen-data benchmark with paired significance tests to separate skill from base-rate artifact.

result Fine-tuned models do not show significant directional skill over a base rate of 70% in financial forecasting.

financial forecasting base-rate artifact LoRA adapter timeseries models directional accuracy

2026-07-14abs ↗pdf ↗

Zero-Shot Forecasting Mortality Rates: A Global Studycs.LG

Study uses zero-shot models to forecast mortality rates globally.

problem Forecasting mortality rates without task-specific fine-tuning.

method Two state-of-the-art foundation models (TimesFM and CHRONOS) and traditional/machine learning methods were evaluated.

result CHRONOS outperformed traditional methods for shorter-term forecasts, but TimesFM consistently underperformed.

zero-shot forecasting mortality rates foundation models time series forecasting horizons

2025-05-17abs ↗pdf ↗

Pretrained Time-Series Foundation Models for Financial Return Forecastingq-fin.MF

Pretrained time-series models outperform train-from-scratch baselines in financial return forecasting.

problem Financial return forecasting

method Pretrained time-series foundation models

result Pretrained TSFMs dominate the ranking distribution, accounting for 8 of 10 task-level wins.

financial return forecasting pretrained models time-series foundation models TSFMs financial markets

2026-06-25abs ↗pdf ↗

Cisco Time Series Model Technical Reportcs.LG

Cisco introduces a new time series model for better forecasting.

problem Improving time series forecasting accuracy.

method Developed a new multiresolution decoder-only model trained on large datasets.

result The new model achieves superior performance on observability datasets.

time series forecasting zero-shot forecasting multiresolution model decoder-only model observability domain

2025-11-25abs ↗pdf ↗

Benchmarking Pre-Trained Time Series Models for Electricity Price Forecastingcs.LG

TSFMs outperform traditional models in electricity price forecasting.

problem Accurate electricity price forecasting for effective decision-making.

method Benchmarking several TSFMs against traditional models using real-world data.

result MSTL model consistently outperforms TSFMs across countries and metrics.

electricity price forecasting time series models genAI pre-trained models seasonality

2025-06-09abs ↗pdf ↗

Time-Series Foundation AI Model for Value-at-Risk Forecastingq-fin.RM

Foundation AI model outperforms traditional VaR methods in forecasting.

problem Forecasting Value-at-Risk (VaR) for financial returns.

method Time-series foundation AI model, pre-trained on diverse datasets, fine-tuned for specific quantiles.

result Fine-tuned foundation model consistently outperforms traditional methods in actual-over-expected ratios.

Value-at-Risk AI model Time-series Quantile forecasting Backtesting

2024-10-15abs ↗pdf ↗

Enhancing Transformer-Based Foundation Models for Time Series Forecasting via Bagging, Boosting and Statistical Ensemblescs.LG

Enhanced TSFMs improve time series forecasting accuracy and reliability.

problem Variance, bias, and uncertainty in TSFMs' predictions on real data.

method Statistical and ensemble techniques including bagging, stacking, residual modeling, and prediction intervals.

result Hybrid models consistently outperform standalone TSFMs across multiple horizons.

time series forecasting foundation models ensemble methods statistical reasoning prediction intervals

2025-08-18abs ↗pdf ↗

ISOMORPH: A Supply Chain Digital Twin for Simulation, Dataset Generation, and Forecasting Benchmarksstat.ML

ISOMORPH creates a digital twin for supply chain logistics, advancing time-series forecasting benchmarks.

problem Lack of public benchmarks for supply chain logistics time-series forecasting.

method Developed a digital twin simulator with interpretable parameters and modular topology, generating datasets and verifying conservation laws.

result Foundation models achieve MASE values exceeding public benchmarks at low-to-moderate horizons, supporting UQ.

Supply Chain Digital Twin Time-Series Forecasting Benchmark Interpretability

2026-05-12abs ↗pdf ↗

Moirai-MoE: Empowering Time Series Foundation Models with Sparse Mixture of Expertscs.LG

Moirai-MoE improves time series forecasting by automatically specializing tokens without human-defined frequency.

problem Unified training on time series data remains challenging due to heterogeneity and non-stationarity.

method Uses sparse mixture of experts (MoE) within Transformers to automatically specialize tokens for diverse time series patterns.

result Moirai-MoE outperforms existing foundation models in both in-distribution and zero-shot scenarios.

time series forecasting foundation models sparse mixture of experts Transformer non-stationarity

2024-10-14abs ↗pdf ↗