The paper calculates prices for multi-step barrier options under the Black-Scholes model.
problem Calculating prices for multi-step barrier options with varying barriers and time steps.
method Derives a general, explicit expression for option prices using the Black-Scholes model and a multi-step reflection principle.
result Derives a multi-step reflection principle that generalizes the reflection principle of Brownian motion.
STG2Seq predicts multi-step passenger demand with graph and hierarchical structure.
problem Predicting passenger demand over multiple time horizons is challenging due to nonlinear and dynamic spatial-temporal dependencies.
method Proposes a graph-based model with a hierarchical graph convolutional structure to capture spatial and temporal correlations.
result Consistently outperforms baseline and state-of-the-art models on real-world datasets.
Paper presents a copula-based method to efficiently generate correlated sample paths from multi-step time series models.
problem Generating realistic correlation structures in multi-step forecast sample paths is expensive and time-consuming.
method Copula-based approach to generate correlated sample paths in one forward pass.
result Improved sample path quality and significant speedup over autoregressive sampling.
A multi-step model reduces compounding errors in reinforcement learning.
problem Compounding errors in one-step models lead to inaccurate predictions in reinforcement learning.
method Introduced a multi-step model that directly outputs the outcome of a sequence of actions.
result The multi-step model yields better action selection and more accurate value-function estimation.
Paper proposes an active multi-step TD algorithm for reinforcement learning.
problem Challenging decision making and control tasks in reinforcement learning.
method Active stepsize learning and adaptive multi-step TD algorithm with context-aware mechanism.
result Competitive results compared to other reinforcement learning baselines on discrete and continuous space tasks.
The paper explores dynamic ensembles for multi-step forecasting.
problem Lack of research on dynamic ensembles for multi-step forecasting.
method Extensive experiments with 3568 time series and an ensemble of 30 multi-output models.
result Dynamic ensembles based on arbitrating and windowing perform best.
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 …
New RL algorithms improve performance using multi-step greedy policies.
problem Improving model-free reinforcement learning performance.
method Developed multi-step greedy κ-Policy Iteration and κ-Value Iteration algorithms for model-free RL. result Multi-step greedy algorithms outperform DQN and TRPO on various benchmark tasks.
The paper introduces a multi-step loss function to improve model-based reinforcement learning.
problem Compounding of one-step prediction errors in long trajectories.
method A multi-step objective function combining MSE losses at various future horizons.
result Models trained with the multi-step loss achieve significant improvement in future prediction.
Paper proposes a dual-level approach for multi-step forecasting of dynamical systems.
problem Accurate multi-step forecasting of time series systems for automatic control and optimization.
method Hybrid input forecasting using LSTM-STMs and physics-informed neural networks (PINNs).
result Hybrid models achieve higher log-likelihood and lower MSE compared to conventional methods.
Efficiently optimizes expensive functions with multi-step lookahead using one-shot optimization.
problem Optimizing expensive functions with long-term impacts using myopic approaches.
method Formulated as nested optimization problems within a multi-step scenario tree, optimized in one-shot fashion.
result Multi-step expected improvement is computationally tractable and outperforms existing methods.
Looped Transformers learn to implement multi-step gradient descent for in-context learning.
problem Understanding the learnability of multi-step algorithms in multi-layer Transformers.
method Training weight-sharing looped Transformers for in-context linear regression, proving gradient dominance condition for convergence.
result Looped Transformers implement multi-step preconditioned gradient descent, converging to global minimizer.
Quantile deep learning improves time series prediction accuracy and uncertainty quantification.
problem Uncertainty in multi-step time series prediction.
method Developed a novel quantile regression deep learning framework for multi-step time series prediction.
result Integrating quantile loss function with deep learning provides additional predictions for selected quantiles without loss in accuracy.
New framework guarantees convergence of multi-step MAML.
problem Convergence of multi-step MAML in nonconvex settings.
method Developed a theoretical framework for two types of MAML objective functions.
result Guaranteed convergence rate and computational complexity for multi-step MAML.
ForecastNet uses a time-variant deep feed-forward neural network for better multi-step-ahead time series forecasting.
problem Time-invariant architectures limit multi-step-ahead forecasting.
method ForecastNet employs a deep feed-forward architecture with time-variant parameters and interleaved outputs.
result ForecastNet outperforms other models on multi-step-ahead time series forecasting tasks.
Paper adapts ACI for online multi-step time-series forecasting with coverage guarantees.
problem Achieving reliable error bounds in online multi-step time-series forecasting.
method Adaptive conformal inference (ACI) adapted for multi-step forecasting with dynamic significance levels.
result Proposes a multi-step ACI algorithm with finite-sample coverage guarantees for non-exchangeable data.
Diffusion-VAE tackles multi-step stock price prediction with stochastic noise.
problem Challenges in multi-step stock price prediction due to stochasticity and target price sequence.
method Combines hierarchical VAE and diffusion probabilistic techniques for seq2seq stock prediction.
result D-Va model outperforms state-of-the-art solutions in prediction accuracy and variance.
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…
Transformers learn multi-step reasoning through gradient descent.
problem Understanding how transformers solve symbolic multi-step reasoning tasks.
method Theoretical analysis of gradient descent dynamics and multi-phase training.
result Trained one-layer transformers can solve both backward and forward reasoning tasks with generalization guarantees.
Proposes a framework to quantify uncertainty in multi-step decision-making by LLMs.
problem Uncertainty quantification in multi-step decision-making scenarios of LLMs.
method A principled, information-theoretic framework decomposing uncertainty into internal and extrinsic components, and proposing UProp for efficient extrinsic uncertainty estimation.
result UProp significantly outperforms existing single-turn UQ baselines in multi-step decision-making benchmarks.
This work analyzes CoT prompting methods from a statistical estimation perspective.
problem Improving the effectiveness of LLMs in solving multi-step reasoning problems.
method Introducing a multi-step latent variable model to characterize CoT prompting from a statistical estimation viewpoint.
result The CoT estimator is equivalent to a Bayesian estimator when the pretraining dataset is large.
Stratify unifies and improves multi-step forecasting strategies.
problem Lack of unified frameworks for multi-step forecasting strategies.
method Proposes Stratify, a parameterized framework for multi-step forecasting.
result Novel strategies in Stratify outperform existing ones in over 84% of experiments.
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…
Proposes a multi-stream RNN model for predicting merchant transactions.
problem Predicting future transaction statistics of merchants.
method Multi-stream RNN model tailored for multivariate time series and multi-step predictions.
result Outperforms existing state-of-the-art methods in merchant transaction predictions.
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 …
The paper addresses Dyna-style RL's value hallucination issue by proposing a new algorithm.
problem Value hallucination in Dyna-style RL due to bootstrapping simulated states.
method Introduces a new Dyna algorithm using predecessor models with multi-step updates.
result Evidence supports the Hallucinated Value Hypothesis (HVH), suggesting predecessor models with multi-step updates are promising.
AEnbMIMOCQR generates robust multi-step ahead prediction intervals for time series data.
problem Generating reliable multi-step ahead prediction intervals for time series data.
method Adaptive ensemble batch multi-input multi-output conformalized quantile regression (AEnbMIMOCQR) based on conformal prediction principles.
result AEnbMIMOCQR provides close to exact coverage and robustness to distribution shifts.
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.
Paper improves multi-step chord prediction in jazz music.
problem Chord sequence prediction accuracy is poor in multi-step scenarios.
method Aggregated multi-scale encoder-decoder network architecture.
result Our model outperforms state-of-the-art methods in accuracy and perplexity.
Proposes a method to improve model-based reinforcement learning by matching multi-step rollout distributions.
problem Mismatching between model-based and real-world rollouts due to estimation error.
method Uses Wasserstein GAN (WGAN) to match the distributions of multi-step rollouts from the learned model to the real ones.
result The method minimizes the difference in cumulative rewards between the real and learned models.
FastVoiceGrad speeds up VC to one step, matching or surpassing quality.
problem Slow inference in multi-step diffusion-based VC.
method Adversarial Conditional Diffusion Distillation (ACDD) for one-step diffusion.
result One-shot VC with superior or comparable performance to multi-step methods.
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…
JANET improves time series prediction with adaptive uncertainty regions.
problem Time series data's lack of exchangeability and multi-step prediction challenges.
method Proposes JANET, a framework for joint adaptive prediction regions with controlled error rates.
result Demonstrates superior performance in multi-step prediction tasks across diverse datasets.
A multi-step framework tackles online unsupervised domain adaptation with novel mean-target subspace computation.
problem Online unsupervised domain adaptation with unlabelled target data arriving sequentially.
method Multi-step framework with a novel mean-target subspace computation and temporal coherency consideration.
result Improved performance over previous approaches on four datasets.
Model forecasts water demand with probabilistic multi-step-ahead approach.
problem Accurate probabilistic forecasts of water demand for operational control.
method Time series model with Lasso for high-dimensional feature space.
result Accurate, interpretable, and fast computable forecasting model.
Agents need world models to generalize multi-step tasks.
problem The necessity of world models for flexible, goal-directed behavior.
method Formal analysis and demonstration of the necessity of world models for agents to generalize multi-step tasks.
result World models are necessary for agents to generalize to multi-step goal-directed tasks.
Proposes QDF to improve multi-step time-series forecasting.
problem Ignoring label autocorrelation and unequal task weights in training objectives.
method Quadratic-form weighted training objective and QDF learning algorithm.
result Improves performance of various forecast models, achieving state-of-the-art results.
Spatiotemporal systems are common in the real-world. Forecasting the multi-step future of these spatiotemporal systems based on the past observations, or, Spatiotemporal Sequence Forecasting (STSF), is a significant and challenging problem. Although lots of real-world problems can be viewed as STSF and many research wo…
Proposes a method to apply conformal prediction to probabilistic time series forecasting models.
problem Obtaining accurate prediction regions for multi-step time series forecasting with probabilistic models.
method Conformalises conditional normalising flows to generate potentially disjoint prediction regions.
result Improves predictive efficiency in time series forecasting with multimodal distributions.
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…
New approaches improve uncertainty quantification in autoregressive models for sequence data.
problem Uncertainty quantification in autoregressive models for exchangeable sequences.
method Study of inferential and architectural biases for autoregressive models, focusing on multi-step inference.
result Custom architectures are necessary for multi-step inference to ensure exchangeability.
This study compares deep learning models for multi-step dissolved oxygen prediction.
problem Lack of comprehensive comparison among deep learning models for multi-step time series forecasting.
method Walk-forward validation using real-time data from 2012 to 2016, tested models: CNN, TCN, LSTM, GRU, BiRNN.
result GRU outperforms other models in multi-step time series forecasting.
We present a numerical framework for approximating unknown governing equations using observation data and deep neural networks (DNN). In particular, we propose to use residual network (ResNet) as the basic building block for equation approximation. We demonstrate that the ResNet block can be considered as a one-step me…
Paper tackles action delays in reinforcement learning, proposing a delay-aware framework.
problem Action delays degrade reinforcement learning performance in real-world systems.
method Formal definition of delay-aware MDP, transformation into standard MDP with augmented states, delay-aware model-based reinforcement learning framework.
result Proposed framework is more efficient in training and transferable between systems with various delay durations.
New algorithm learns optimal policy with multi-step lookahead information.
problem Learning optimal policy in reinforcement learning with multi-step lookahead information is NP-hard.
method Adaptive batching policies that process lookahead in state-dependent chunks.
result Order-optimal regret bounds up to a constant factor of lookahead horizon.
Novel unsupervised feature selection method using multi-step Markov transition probability.
problem Neglected relationships between non-adjacent data points in feature selection.
method MMFS (Multi-step Markov transition probability for Feature Selection) approach, employing positive and negative viewpoints.
result MMFS effectively maintains data structure in unsupervised feature selection.
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…
In recent years, deep neural networks have demonstrated outstanding performance in many machine learning tasks. However, researchers have discovered that these state-of-the-art models are vulnerable to adversarial examples: legitimate examples added by small perturbations which are unnoticeable to human eyes. Adversari…