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.
Improved forecasting in daily time series competition using a correlator method.
New forecasting framework sktime replicates and improves M4 study results.
Deep learning improves time series forecasting, outperforming other methods.
We propose a novel parameterized family of Mixed Membership Mallows Models (M4) to account for variability in pairwise comparisons generated by a heterogeneous population of noisy and inconsistent users. M4 models individual preferences as a user-specific probabilistic mixture of shared latent Mallows components. Our k…
Kaggle competitions offer valuable insights for business forecasting.
We focus on solving the univariate times series point forecasting problem using deep learning. We propose a deep neural architecture based on backward and forward residual links and a very deep stack of fully-connected layers. The architecture has a number of desirable properties, being interpretable, applicable withou…
Recurrent Neural Networks (RNN) have become competitive forecasting methods, as most notably shown in the winning method of the recent M4 competition. However, established statistical models such as ETS and ARIMA gain their popularity not only from their high accuracy, but they are also suitable for non-expert users as…
Due to their prevalence, time series forecasting is crucial in multiple domains. We seek to make state-of-the-art forecasting fast, accessible, and generalizable. ES-RNN is a hybrid between classical state space forecasting models and modern RNNs that achieved a 9.4% sMAPE improvement in the M4 competition. Crucially, …
This paper presents a time series forecasting framework which combines standard forecasting methods and a machine learning model. The inputs to the machine learning model are not lagged values or regular time series features, but instead forecasts produced by standard methods. The machine learning model can be either a…
Feature-based time series representations have attracted substantial attention in a wide range of time series analysis methods. Recently, the use of time series features for forecast model averaging has been an emerging research focus in the forecasting community. Nonetheless, most of the existing approaches depend on …
The authors argue against the classification of forecasting methods as machine learning or statistical.
Robust forecast framework reduces distribution error by 63%.
This paper introduces a deep learning ensemble forecasting model using Dirichlet process.
This paper won 1st place in forecasting and investment challenges, improving on meta-learning and parametric models.
The study evaluates forecast risk-adjusted performance using various metrics.
Topological attention improves forecasting of univariate time series.
Generating forecasts for time series with multiple seasonal cycles is an important use-case for many industries nowadays. Accounting for the multi-seasonal patterns becomes necessary to generate more accurate and meaningful forecasts in these contexts. In this paper, we propose Long Short-Term Memory Multi-Seasonal Net…
HERMES model predicts nonstationary fashion trends using social media data.
Meta-learning predicts optimal ensemble size and methods for time series forecasting.
Optimizes forecast accuracy and diversity using multi-task deep learning.
A new framework for time series analysis using state-space learning.
Low-power sensing technologies, such as wearables, have emerged in the healthcare domain since they enable continuous and non-invasive monitoring of physiological signals. In order to endow such devices with clinical value, classical signal processing has encountered numerous challenges. However, data-driven methods, s…
The implementation of Deep Convolutional Neural Networks (ConvNets) on tiny end-nodes with limited non-volatile memory space calls for smart compression strategies capable of shrinking the footprint yet preserving predictive accuracy. There exist a number of strategies for this purpose, from those that play with the to…
New solutions of gravity from branes wrapped on orbifolds.
Proposes CoPO, a new policy optimization method for competitive games.
The origin of economic crises is a key problem for economics. We present a model of long-run competitive markets to show that the multiplicity of behaviors in an economic system, over a long time scale, emerge as statistical regularities (perfectly competitive markets obey Bose-Einstein statistics and purely monopolist…
New framework promotes reproducible, domain-agnostic reinforcement learning algorithms.
The real estate is a pillar industry of China's national economy. Due to changes in policy and market conditions, the real estate companies are facing greater pressures to survive in a competitive environment. They must improve their financial competitiveness. Based on the conceptual framework of financial competitiven…
The Affective Behavior Analysis in-the-wild (ABAW) 2020 Competition is the first Competition aiming at automatic analysis of the three main behavior tasks of valence-arousal estimation, basic expression recognition and action unit detection. It is split into three Challenges, each one addressing a respective behavior t…
Study examines machine learning competitions' impact on AI development.
Bayesian rating system for large competitions improves prediction and efficiency.
MineRL Competition reduced reinforcement learning sample needs.
This paper evaluates financial competitiveness of Indian real estate companies using entropy method.
In this paper, the optimal pricing strategy in Avellande-Stoikov's for a monopolistic dealer is extended to a general situation where multiple dealers are present in a competitive market. The dealers' trading intensities, their optimal bid and ask prices and therefore their spreads are derived when the dealers are info…
Model predicts growth competition on curved surfaces.
TailedTS dataset benchmarks heavy-tailed time series forecasting and periodicity quantification.
Market competition depends on computational complexity, P != NP makes it impossible.
The M5 competition tackles overdispersed retail sales forecasting with GAMLSS.
Model predicts competition between similar products in sales.
The NIPS 2018 Adversarial Vision Challenge is a competition to facilitate measurable progress towards robust machine vision models and more generally applicable adversarial attacks. This document is an updated version of our competition proposal that was accepted in the competition track of 32nd Conference on Neural In…
Data competitions rely on real-time leaderboards to rank competitor entries and stimulate algorithm improvement. While such competitions have become quite popular and prevalent, particularly in supervised learning formats, their implementations by the host are highly variable. Without careful planning, a supervised lea…
We introduce an irreversible discrete multiplicative process that undergoes Bose-Einstein condensation as a generic model of competition. New players with different abilities successively join the game and compete for limited resources. A player's future gain is proportional to its ability and its current gain. The the…
Politicians world-wide frequently promise a better life for their citizens. We find that the probability that a country will increase its {\it per capita} GDP ({\it gdp}) rank within a decade follows an exponential distribution with decay constant . We use the Corruption Perceptions Index (CPI) and the Global …
We present a broad agenda for meaningful banking regulation reform aiming the creation of evolutive competitive environment to maximize the effectiveness of international financial system through the introduction of fair competition process among the banks in free market capitalism. We assume that the international fin…
FLAIR measures LP competitiveness in AMMs, improving LP performance evaluations.
Kaggle chronicles 15 years of competitions, innovation, and data science.
A competition increases financial transaction models' robustness against attacks.
This work bridges competitive learning with gradient-based learning for faster feature extraction.