arXiv AI

The Remittance Blueprint: Data-driven Intelligence for Sri Lanka

arXiv:2606. 28190v1 Announce Type: cross Abstract: This study analyzes Sri Lankan migration and remittances over 32 years (1994-2025).

arXiv Machine Learning

Jun 30

When Prices Double in a Week: Forecasting of Agricultural Volatility in Import-Isolated Markets

arXiv:2606. 29248v1 Announce Type: new Abstract: Vegetable prices in Sri Lanka are highly volatile because the market is largely import-isolated, so supply disruptions quickly drive prices up.

By Ranuga Weerasekara, Heshan Nethmina, Manuja Ranathunga, Vinma Wettasinghe, Dinithi Navodya, Subavarshana Arumugam, Nirasha Munasinghe, Nisansa de Silva, Sandareka Wickramanayake

computer-vision

arXiv Machine Learning

Jun 16

Machine Learning and the Random Walk Puzzle: Forecasting the CAD/USD Exchange Rate with Expanding Window Evaluation and SHAP Interpretability

arXiv:2606. 15058v1 Announce Type: new Abstract: This study examines whether machine learning (ML) models can outperform the naive random walk benchmark in forecasting the monthly USD/CAD exchange rate.

By Louis Agyekum, Edmund Fosu Agyemang, Obu-Amoah Ampomah, Kofi Acheampong, Emmanuel Boadi, Priscilla Yaa Amakye, Fafa Shalom Tchorly, Enock Adu Bonsu, Eric Nyarko

benchmarks safety

arXiv Machine Learning

Jun 5

Electricity price forecasting across Norway's five bidding zones in the post-crisis era

arXiv:2604. 26634v2 Announce Type: replace Abstract: Norway's electricity market is heavily dominated by hydropower, but the 2021-2022 energy crisis and stronger integration with Continental Europe have fundamentally altered price formation, reducing the reliability of forecasting models calibrated on historical data.

By My Thi Diem Phan, Trung Tuyen Truong, Hoai Phuong Ha, Dat Thanh Nguyen

multimodal benchmarks safety

arXiv Machine Learning

Aug 6

Monsoon Mayhem to Market Waves: Forecasting Fisheries Resilience in Sri Lanka

arXiv:2608. 04023v1 Announce Type: cross Abstract: Sri Lanka's fisheries sector is important for jobs and food supply.

By Ruzaini Ahmed, Yohan Jayasinghe, Tharumini Gamage, Ifaz Ikram, Hasini Lawanya, Nirasha Munasinghe, Patalee Narasinghe, Nisansa de Silva, Sandareka Wickramanayake

arXiv Machine Learning

Jul 7

Yield Curves Dynamics Using Variational Autoencoders Under No-arbitrage

arXiv:2605. 12764v3 Announce Type: replace-cross Abstract: This paper introduces a physics-informed generative framework that resolves the fundamental conflict between the statistical flexibility of deep learning and the rigorous theoretical constraints of fixed-income modeling.

By Fusheng Luo, H'elyette Geman

arXiv Machine Learning

Jun 3

FinStressTS: A Parametric Synthetic Benchmark for Time-Series Forecasting in Finance

arXiv:2606. 03184v1 Announce Type: cross Abstract: Financial forecasting is difficult due to low signal-to-noise ratios, latent factors, heavy tails, regime shifts, and jumps.

By Jiaze Sun, Kelvin J. L. Koa, Ruiyang Ni, Yize Liu, Haonan Chen, Ke-Wei Huang

llms multimodal benchmarks safety

arXiv Machine Learning

1d ago

Detecting Money Laundering in Rwandan Mobile Money: A Machine Learning Framework

arXiv:2608. 15447v1 Announce Type: new Abstract: Mobile money has widened financial access across Sub-Saharan Africa and enlarged the surface for money-laundering and terrorism-financing (ML/TF) activity in ecosystems dominated by high-volume, low-value transactions.

By Emmanuel Nahimana, Ya\'e Ulrich Gaba

benchmarks

arXiv Machine Learning

Jun 15

Neural ARFIMA model for forecasting BRIC exchange rates with long memory

arXiv:2509. 06697v3 Announce Type: replace-cross Abstract: Exchange rate forecasting remains a challenging problem, particularly for emerging economies, where the observed time series exhibit pronounced long-memory dependence, nonlinear dynamics, and sensitivity to macro-financial drivers.

By Donia Besher, Madhurima Panja, Shovon Sengupta, Tanujit Chakraborty

benchmarks

arXiv AI

Jul 28

AIFL: A Global Daily Streamflow Forecasting Model Using a Deterministic LSTM Pre-trained on ERA5-Land and Fine-tuned on IFS

arXiv:2602. 16579v2 Announce Type: replace-cross Abstract: Reliable global streamflow forecasting is essential for flood preparedness and water resource management, yet data-driven models often suffer from a performance gap when transitioning from historical reanalysis to operational forecast products.

By Maria Luisa Taccari, Kenza Tazi, Ois\'in M. Morrison, Andreas Grafberger, Juan Colonese, Corentin Carton de Wiart, Christel Prudhomme, Cinzia Mazzetti, Matthew Chantry, Florian Pappenberger

fine-tuning benchmarks

arXiv AI

Jul 14

SciML in the Wild: A Diagnostic Study of When Structural Priors Help and When They Hurt

arXiv:2607. 09684v1 Announce Type: cross Abstract: Scientific Machine Learning (SciML) methods such as Neural Ordinary Differential Equations (NODEs), Physics-Informed Neural Networks (PINNs), and Universal Differential Equations (UDEs) are most effective when structural priors reflect reliable governing dynamics.

By Vrishank Sai Anand, Prathamesh Dinesh Joshi, Raj Abhijit Dandekar, Rajat Dandekar, Sreedath Panat

arXiv Machine Learning

Aug 5

Cross-Country Learning for National Infectious Disease Forecasting Using European Data

arXiv:2601. 20771v2 Announce Type: replace-cross Abstract: Accurate forecasting of infectious disease incidence is critical for public health planning and timely intervention.

By Zacharias Komodromos, Kleanthis Malialis, Artemis Kontou, Panayiotis Kolios

arXiv Machine Learning

Jun 18

RNN(p) for Power Consumption Forecasting

arXiv:2209. 01378v3 Announce Type: replace Abstract: An elementary Recurrent Neural Network that operates on p time lags, called an RNN(p), is the natural generalisation of a linear autoregressive model ARX(p).

By Roberto Baviera, Pietro Manzoni

safety

Related stories