Investigates fund separations and stability for long-term optimal investments.
problem Optimizing long-term investments in an incomplete market with risky and safe assets.
method Analyzes three market models with different state variable processes to find optimal portfolios and prove convergence stability.
result Dynamic optimal portfolios converge to static portfolios over time, with vanishing sensitivities in the long run.
Noise-robust Koopman operator framework for control with improved stability and performance.
problem Developing a stable and noise-robust Koopman operator for control tasks.
method Proposes a learning framework using Hankel matrix and neural network approximations for system dynamics, ensuring long-term stability and noise robustness.
result Demonstrates improved model performance and noise robustness in control tasks compared to existing methods.
Log-ergodic model improves velocity of money prediction.
problem Improving velocity of money prediction for economic control.
method Log-ergodic processes to simulate monetary velocity.
result Log-ergodic model offers superior predictive power.
Study uses VIX for zero-coupon Treasury rates, proving long-term stability and returns.
problem Modeling zero-coupon Treasury rates with VIX for volatility.
method Multivariate autoregressive stochastic volatility model, proving stability and Law of Large Numbers.
result VIX accurately models zero-coupon Treasury rates and returns.
Stable long-term predictions for fluid flows using neural networks.
problem Predicting complex dynamics of fluid flows with high temporal stability.
method End-to-end trained neural network architecture combining CNN for spatial compression and LSTM for temporal prediction.
result Novel latent space subdivision (LSS) allows stable and controllable long-term predictions.
In this paper, we propose a stochastic investment model for actuarial use in South Africa by modelling price inflation rates, share dividends, long term and short-term interest rates for the period 1960-2018 and inflation-linked bonds for the period 2000-2018. Possible bi-directional relations between the economic seri…
Study shows how competition affects learning in matching markets, proving it's possible to balance stability, fairness, and regret.
problem How competition affects learning in matching markets and the impossibility of simultaneously guaranteeing stability and low optimal regret.
method Modeling a two-sided matching market with bandit learners and adding components of costs and transfers.
result It is possible to simultaneously guarantee stability, low optimal regret, fairness in the distribution of regret, and high social welfare.
The paper analyzes Indian stock sectors using multifractal analysis for long and short-term investment.
problem Investment risk and stability in Indian stock sectors.
method Sector-wise multifractal analysis of Bombay Stock Exchange, India, over short and long time scales.
result Long-term investment in stable sectors is more profitable, while sectors with large fluctuations may lead to downturns.
The paper addresses estimating long-term treatment effects with monotone missing data.
problem Estimating long-term treatment effects with missing data, especially monotone missing.
method The paper introduces the sequential missingness assumption for identification and proposes three novel estimation methods: inverse probability weighting, sequential regression imputation, and SeqMSM. It also introduces a balancing-enhanced approach, BalanceNet, to improve estimation accuracy.
result The proposed methods, including BalanceNet, effectively estimate long-term treatment effects with monotone missing data.
Study adds memory effect to Solow-Swan model for more accurate economic growth modeling.
problem Inaccuracies in classical Solow-Swan model in capturing long-term dynamics.
method Introduced fractional calculus with Caputo derivative into Solow-Swan framework.
result Fractional-order model shows significant impact on capital accumulation and stability.
Decentralized learning ensures stability in online queuing systems with packet rates above 1.
problem Ensuring stability in online queuing systems with decentralized learning.
method Proposed cooperative queues and a learning algorithm for packet rates above 1.
result Decentralized learning strategies guarantee stability in queuing systems with packet rates above 1.
Stability proved for open Milne spacetime, showing gravity's long-term behavior.
problem Global stability of open Milne spacetime for Einstein-scalar field equations.
method Gaussian normal coordinates, exploiting expanding geometry of Milne spacetime.
result Spatial metric tends to hyperbolic metric as time goes to infinity.
The study improves stock market valuation using volatility and earnings data.
problem Improving stock market valuation metrics.
method Time series model for asset returns, multivariate kernel density estimation, linear regression.
result The valuation measure is an improvement over Shiller's P/E ratio.
Recurrent neural networks have gained widespread use in modeling sequential data. Learning long-term dependencies using these models remains difficult though, due to exploding or vanishing gradients. In this paper, we draw connections between recurrent networks and ordinary differential equations. A special form of rec…
Study improves reinforcement learning for stable long-term performance.
problem Distributionally robust average-reward reinforcement learning for stable long-term performance.
method Proposes two algorithms to achieve near-optimal sample complexity.
result Achieves a sample complexity of O(∣S∣∣A∣tmix2ε−2) for estimating optimal policy and robust average reward. Bayesian model predicts interest rates with short-term accuracy and long-term stability.
problem Improving short- and long-term prediction of time series with temporary non-stationary behavior.
method Time-varying autoregressive model with Bayesian regularization and MCMC inference.
result Model outperforms existing methods in both short and long-term predictions.
Generative framework learns effective, lower-dimensional models from high-dimensional data.
problem Predicting long-term behavior of complex, multiscale systems with limited data.
method Physics-aware probabilistic model order reduction with latent variables.
result Guaranteed long-term stability and predictive accuracy in multiscale physical systems.
EuSN uses Euler discretization for stable, non-dissipative reservoir computing.
problem Designing stable and efficient reservoir computing models.
method Forward Euler discretization and antisymmetric recurrent matrices.
result EuSN outperforms standard RC models in long-term memory tasks and time-series classification.
The paper models financial markets and real economy interactions using a large agent framework.
problem Understanding capital allocation and accumulation in financial markets and real economy interactions.
method Developed a field-formalism model to analyze interactions between financial markets and real economy with a large number of heterogeneous agents.
result The number of firms in each sector depends on the aggregate financial capital invested and expected long-term returns.
Thermalizer stabilizes autoregressive models for long-term predictions in chaotic systems.
problem Long-term predictions in chaotic spatiotemporal systems are unreliable due to trajectory divergence.
method Diffusion models are used to implicitly estimate the score of an invariant measure, which stabilizes autoregressive emulators by applying denoising during inference.
result Thermalization extends the time horizon of stable predictions by an order of magnitude in chaotic systems.
We present a simple quantile regression-based forecasting method that was applied in a probabilistic load forecasting framework of the Global Energy Forecasting Competition 2017 (GEFCom2017). The hourly load data is log transformed and split into a long-term trend component and a remainder term. The key forecasting ele…
Lipschitz RNNs improve stability and performance in various tasks.
problem Improving stability and performance of RNNs.
method Introduced a Lipschitz recurrent unit with a linear and Lipschitz nonlinear component for stability analysis.
result Lipschitz RNNs outperform existing units on benchmark tasks.
A new method distills datasets more efficiently and effectively.
problem Achieving competitive performance on test data with a small synthetic dataset.
method Tackles dataset distillation as a bilevel optimization problem, introduces RaT-BPTT to stabilize gradients and speed up optimization.
result Establishes new state-of-the-art performance across various benchmarks.
Proposes a method to learn stable invariant sets in dynamical systems.
problem Learning stable invariant sets in general dynamical systems.
method Generalizes Manek and Kolter's approach by introducing projection onto latent space shapes and using invertible neural networks.
result Validates the method and shows its usefulness for long-term prediction.
The paper analyzes how mutable blockchain protocols affect miner behavior and strategic stability.
problem The mutability of blockchain protocols undermines long-term planning and cooperative equilibria.
method Integrates Austrian capital theory with repeated game theory to examine miner behavior under different institutional conditions.
result Effective time preference increases when protocol rules are mutable, leading to political rent-seeking and undermining strategic coherence.
In the current environment of financial distress, many governments are likely to soon become major holders of financial assets, but the policy debate focuses only on the likelihood and extent of short-term market stabilization. This paper shows that government intervention and propping up are likely to lead to long-ter…
Economic growth is unpredictable unless demand is quantified. We solve this problem by introducing the demand for unpaid spare time and a user quantity named human capacity. It organizes and amplifies spare time required for enjoying affluence like physical capital, the technical infrastructure for production, organize…
This paper challenges the current metrics used for evaluating long-term forecasting models.
problem Current metrics focus on pointwise error reduction, ignoring structural properties.
method Proposes a multi-dimensional evaluation approach that includes statistical fidelity, structural coherence, and decision-level relevance.
result Current progress in forecasting may reflect specialization in benchmark configurations rather than deeper understanding of temporal dynamics.
It is a known fact that training recurrent neural networks for tasks that have long term dependencies is challenging. One of the main reasons is the vanishing or exploding gradient problem, which prevents gradient information from propagating to early layers. In this paper we propose a simple recurrent architecture, th…
Proposes a variational autoencoder for long-term customer revenue forecasting.
problem Predicting long-term customer revenue from sparse and irregular transaction data.
method Variational Autoencoder (VAE) with flexible latent representation.
result Improves upon latest benchmarks in multiple real-world datasets.
We develop a model of stable assets, including non-custodial stablecoins backed by cryptocurrencies. Such stablecoins are popular methods for bootstrapping price stability within public blockchain settings. We derive fundamental results about dynamics and liquidity in stablecoin markets, demonstrate that these markets …
We introduce here for the first time the long-term swap rate, characterised as the fair rate of an overnight indexed swap with infinitely many exchanges. Furthermore we analyse the relationship between the long-term swap rate, the long-term yield, see Biagini et al. [2018], Biagini and Härtel [2014], and El Karoui et a…
Kernel method estimates long-term effects from short-term data.
problem Estimating long-term effects from short-term data in continuous actions.
method Kernel ridge regression to embed and extrapolate long-term effects.
result Uniform consistency and nonasymptotic error bounds for the estimator.
Koopman-PINN framework improves epidemic model parameter inference and forecasting
problem Epidemic model parameter inference and forecasting
method Combining Koopman operator theory and physics-informed learning
result More accurate parameter estimation, trajectory reconstruction, and long-term forecasting
Theoretical study shows AI models can recover from contaminated training data.
problem Data contamination in AI training can degrade model performance.
method Theoretical analysis and experiments on various data types.
result Models converge to true distribution under mild conditions, with rate dependent on real data fraction.
Q(Δ)-Learning improves Q-Learning by separating action-value functions into different time scales.
problem Q-Learning struggles with bias-variance trade-off, especially in long-term rewards.
method Introduces Q(Δ)-Learning, extending TD(Δ) to decompose Q(Δ)-function into distinct discount factors. result Q(Δ)-Learning achieves better stability and scalability, especially for long-term tasks. Model combines long-term and short-term memory using conceptors.
problem Transfer between long-term and short-term memory.
method Recurrent neural network with gated reservoir for short-term memory and conceptors for long-term memory.
result Standard operations on conceptors allow combining long-term memories and describing their effect on short-term memory.
New framework estimates long-term outcomes from short-term data.
problem Estimating long-term outcomes from short-term data.
method Reward function decomposition-based framework (LOPE).
result LOPE outperforms existing methods, especially when surrogacy is violated.
TimeBridge addresses non-stationarity in long-term time series forecasting.
problem Non-stationarity in multivariate time series leads to spurious regressions and obscures long-term relationships.
method TimeBridge segments series into patches, applying Integrated Attention for short-term non-stationarity and Cointegrated Attention for long-term cointegration.
result TimeBridge achieves state-of-the-art performance in both short-term and long-term forecasting.
We collect and analyze the data for working time, life expectancy, and the pair output and infrastructure of industrializing nations. During S-functional recovery from disaster the pair's time shifts yield 25 years for the infrastructure's physical lifetime. At G7 level the per capita outputs converge and the time shif…
This paper balances short-term and long-term rewards in policy learning.
problem Balancing short-term and long-term rewards in policy learning.
method Formalizes a new framework to balance rewards, identifies rewards under mild assumptions, deduces efficiency bounds, and develops a policy learning approach.
result The proposed method improves the estimator of long-term reward and reduces regret.
Study on stochastic mean curvature flow on networks using Ito calculus.
problem Understanding the dynamics of network structures under random influences.
method Application of Ito calculus to derive a stochastic differential equation (SDE) for network edges.
result New insights into the stability, long-term behavior, and pattern formation of complex networks under stochastic influences.
We study the long-term memory in diverse stock market indices and foreign exchange rates using the Detrended Fluctuation Analysis(DFA). For all daily and high-frequency market data studied, no significant long-term memory property is detected in the return series, while a strong long-term memory property is found in th…
This paper uses Bayesian models to analyze CTA returns across short and long-term trends.
problem The relative merits and interactions of short- and long-term trend systems in CTA replication remain controversial.
method Dynamic decomposition of CTA returns into short-term trend, long-term trend, and market beta factors using a Bayesian graphical model.
result The blend of horizons shapes the strategy's risk-adjusted performance.
Estimates long-term effects from short-term experiments and observational data with unobserved confounders.
problem Estimating long-term causal effects from short-term experiments and long-term observational data with unobserved confounding.
method Combining regression residuals with short-term experimental outcomes to create an instrumental variable for estimating long-term causal effects.
result The estimator is unbiased and its variance is analytically studied.
The paper tackles long-term treatment effects with persistent confounders using sequential short-term outcomes.
problem Estimating long-term treatment effects with persistent unmeasured confounders.
method Exploiting the sequential structure of short-term outcomes, the paper develops three novel identification strategies and corresponding estimators.
result The proposed methods outperform existing approaches in handling persistent confounders.
TiDE uses MLP for fast, simple long-term time-series forecasting.
problem Long-term time-series forecasting challenges.
method Time-series Dense Encoder (TiDE) based on MLP.
result TiDE matches or outperforms Transformer models while being 5-10x faster.
In this paper we provide compelling evidence of cyclical mean reversion and multiperiod stock return predictability over horizons of about 30 years with a half-life of about 15 years. This implies that the US stock market follows a long-term rhythm where a period of above average returns tends to be followed by a perio…