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.
Paper adapts ACI for online multi-step time-series forecasting with coverage guarantees.
The paper explores dynamic ensembles for multi-step forecasting.
Multi-step ahead forecasting is still an open challenge in time series forecasting. Several approaches that deal with this complex problem have been proposed in the literature but an extensive comparison on a large number of tasks is still missing. This paper aims to fill this gap by reviewing existing strategies for m…
AEnbMIMOCQR generates robust multi-step ahead prediction intervals for time series data.
Multi-step-ahead time series prediction is one of the most challenging research topics in the field of time series modeling and prediction, and is continually under research. Recently, the multiple-input several multiple-outputs (MISMO) modeling strategy has been proposed as a promising alternative for multi-step-ahead…
Quantile deep learning improves time series prediction accuracy and uncertainty quantification.
Accurate time series prediction over long future horizons is challenging and of great interest to both practitioners and academics. As a well-known intelligent algorithm, the standard formulation of Support Vector Regression (SVR) could be taken for multi-step-ahead time series prediction, only relying either on iterat…
This study proposes methods for multi-step-ahead stock price prediction using decomposition and neural networks.
Model forecasts water demand with probabilistic multi-step-ahead approach.
When environmental interaction is expensive, model-based reinforcement learning offers a solution by planning ahead and avoiding costly mistakes. Model-based agents typically learn a single-step transition model. In this paper, we propose a multi-step model that predicts the outcome of an action sequence with variable …
BCI provides calibrated prediction intervals for time series forecasts.
Recurrent and convolutional neural networks are the most common architectures used for time series forecasting in deep learning literature. These networks use parameter sharing by repeating a set of fixed architectures with fixed parameters over time or space. The result is that the overall architecture is time-invaria…
MLMC boosts Bayesian optimization's look-ahead efficiency.
A new framework evaluates deep learning vs classical forecasting methods for time series predictions.
Study non-parametric value function estimation from a single path.
JANET improves time series prediction with adaptive uncertainty regions.
The problem of automatic and accurate forecasting of time-series data has always been an interesting challenge for the machine learning and forecasting community. A majority of the real-world time-series problems have non-stationary characteristics that make the understanding of trend and seasonality difficult. Our int…
Adaptive optimal control of nonlinear dynamic systems with deterministic and known dynamics under a known undiscounted infinite-horizon cost function is investigated. Policy iteration scheme initiated using a stabilizing initial control is analyzed in solving the problem. The convergence of the iterations and the optim…
Despite the superiority of convolutional neural networks demonstrated in time series modeling and forecasting, it has not been fully explored on the design of the neural network architecture and the tuning of the hyper-parameters. Inspired by the incremental construction strategy for building a random multilayer percep…
MAGMA uses a common mean process to improve multi-step-ahead time series forecasting.
This paper presents a novel Inter Catchment Wastewater Transfer (ICWT) method for mitigating sewer overflow. The ICWT aims at balancing the spatial mismatch of sewer flow and treatment capacity of Wastewater Treatment Plant (WWTP), through collaborative operation of sewer system facilities. Using a hydraulic model, the…
New models analyze how ECB's unconventional policies affect stock market volatility.
The paper calculates prices for multi-step barrier options under the Black-Scholes model.
The Teacher Forcing algorithm trains recurrent networks by supplying observed sequence values as inputs during training and using the network's own one-step-ahead predictions to do multi-step sampling. We introduce the Professor Forcing algorithm, which uses adversarial domain adaptation to encourage the dynamics of th…
Efficiently optimizes expensive functions with multi-step lookahead using one-shot optimization.
Deep state space model forecasts time series with uncertainty.
Stanza models complex time series with balance between traditional and deep learning approaches.
Paper presents a copula-based method to efficiently generate correlated sample paths from multi-step time series models.
Framework predicts and prepares for rain-induced microwave link attenuation.
Planning has been very successful for control tasks with known environment dynamics. To leverage planning in unknown environments, the agent needs to learn the dynamics from interactions with the world. However, learning dynamics models that are accurate enough for planning has been a long-standing challenge, especiall…
Reinforcement learning has attracted great attention recently, especially policy gradient algorithms, which have been demonstrated on challenging decision making and control tasks. In this paper, we propose an active multi-step TD algorithm with adaptive stepsizes to learn actor and critic. Specifically, our model cons…
Study shows optimal RL with transition look-ahead is NP-hard for .
Stratify unifies and improves multi-step forecasting strategies.
Multi-step passenger demand forecasting is a crucial task in on-demand vehicle sharing services. However, predicting passenger demand over multiple time horizons is generally challenging due to the nonlinear and dynamic spatial-temporal dependencies. In this work, we propose to model multi-step citywide passenger deman…
The paper introduces a multi-step loss function to improve model-based reinforcement learning.
Looped Transformers learn to implement multi-step gradient descent for in-context learning.
Multi-step temporal difference (TD) learning is an important approach in reinforcement learning, as it unifies one-step TD learning with Monte Carlo methods in a way where intermediate algorithms can outperform either extreme. They address a bias-variance trade off between reliance on current estimates, which could be …
Paper proposes a dual-level approach for multi-step forecasting of dynamical systems.
For the efficient compensation of fiber nonlinearity, one of the guiding principles appears to be: fewer steps are better and more efficient. We challenge this assumption and show that carefully designed multi-step approaches can lead to better performance-complexity trade-offs than their few-step counterparts.
Model-based reinforcement learning is an appealing framework for creating agents that learn, plan, and act in sequential environments. Model-based algorithms typically involve learning a transition model that takes a state and an action and outputs the next state---a one-step model. This model can be composed with itse…
SA-PEF improves federated learning efficiency by correcting gradient mismatches.
New acquisition functions improve Bernoulli LSE.
New methods for scoring function decomposition improve forecast evaluation.
In its simplest form, the traffic flow prediction problem is restricted to predicting a single time-step into the future. Multi-step traffic flow prediction extends this set-up to the case where predicting multiple time-steps into the future based on some finite history is of interest. This problem is significantly mor…
Diffusion-VAE tackles multi-step stock price prediction with stochastic noise.
Transformers learn multi-step reasoning through gradient descent.
The task of multi-step ahead prediction in language models is challenging considering the discrepancy between training and testing. At test time, a language model is required to make predictions given past predictions as input, instead of the past targets that are provided during training. This difference, known as exp…
The paper develops a method to forecast financial risk multiple steps ahead using quantile time series and historical simulation.