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 presents a copula-based method to efficiently generate correlated sample paths from multi-step time series models.
Paper adapts ACI for online multi-step time-series forecasting with coverage guarantees.
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…
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…
Diffusion-VAE tackles multi-step stock price prediction with stochastic noise.
Quantile deep learning improves time series prediction accuracy and uncertainty quantification.
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 …
JANET improves time series prediction with adaptive uncertainty regions.
Paper proposes a dual-level approach for multi-step forecasting of dynamical systems.
Proposes a multi-stream RNN model for predicting merchant transactions.
AEnbMIMOCQR generates robust multi-step ahead prediction intervals for time series data.
We investigate the multi-step prediction of the drivable space, represented by Occupancy Grid Maps (OGMs), for autonomous vehicles. Our motivation is that accurate multi-step prediction of the drivable space can efficiently improve path planning and navigation resulting in safe, comfortable and optimum paths in autonom…
The paper introduces a multi-step loss function to improve model-based reinforcement learning.
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…
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…
Proposes a method to apply conformal prediction to probabilistic time series forecasting models.
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.
Stratify unifies and improves multi-step forecasting strategies.
BCI provides calibrated prediction intervals for time series forecasts.
In many forecasting applications, it is valuable to predict not only the value of a signal at a certain time point in the future, but also the values leading up to that point. This is especially true in clinical applications, where the future state of the patient can be less important than the patient's overall traject…
Model predicts stock price changes and forecasts using tokenized data.
The paper calculates prices for multi-step barrier options under the Black-Scholes model.
Efficiently optimizes expensive functions with multi-step lookahead using one-shot optimization.
Representations of sequential data are commonly based on the assumption that observed sequences are realizations of an unknown underlying stochastic process, where the learning problem includes determination of the model parameters. In this context the model must be able to capture the multi-modal nature of the data, w…
The paper explores dynamic ensembles for multi-step forecasting.
We report on a technique based on multi-agent games which has potential use in the prediction of future movements of financial time-series. A third-party game is trained on a black-box time-series, and is then run into the future to extract next-step and multi-step predictions. In addition to the possibility of identif…
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…
Agents need world models to generalize multi-step tasks.
Point forecasting of univariate time series is a challenging problem with extensive work having been conducted. However, nonparametric probabilistic forecasting of time series, such as in the form of quantiles or prediction intervals is an even more challenging problem. In an effort to expand the possible forecasting p…
New algorithm learns optimal policy with multi-step lookahead information.
Paper explains DRL strategies for portfolio management using linear models.
This paper studies the prediction of chord progressions for jazz music by relying on machine learning models. The motivation of our study comes from the recent success of neural networks for performing automatic music composition. Although high accuracies are obtained in single-step prediction scenarios, most models fa…
Deep learning has achieved impressive prediction performance in the field of sequence learning recently. Dissolved oxygen prediction, as a kind of time-series forecasting, is suitable for this technique. Although many researchers have developed hybrid models or variant models based on deep learning techniques, there is…
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 …
Framework predicts and prepares for rain-induced microwave link attenuation.
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.
New framework guarantees convergence of multi-step MAML.
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…
ForecastNet uses a time-variant deep feed-forward neural network for better multi-step-ahead time series forecasting.
A multi-step framework tackles online unsupervised domain adaptation with novel mean-target subspace computation.
Transformers learn multi-step reasoning through gradient descent.
Proposes a framework to quantify uncertainty in multi-step decision-making by LLMs.
Multi-step greedy policies have been extensively used in model-based reinforcement learning (RL), both when a model of the environment is available (e.g.,~in the game of Go) and when it is learned. In this paper, we explore their benefits in model-free RL, when employed using multi-step dynamic programming algorithms: …
We examine the impact of learning Lipschitz continuous models in the context of model-based reinforcement learning. We provide a novel bound on multi-step prediction error of Lipschitz models where we quantify the error using the Wasserstein metric. We go on to prove an error bound for the value-function estimate arisi…
A new framework evaluates deep learning vs classical forecasting methods for time series predictions.
This work analyzes CoT prompting methods from a statistical estimation perspective.