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…
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26 results for “multi-step-ahead”
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.
A general framework for multi-step ahead adaptive conformal heteroscedastic time series forecastingstat.ML
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.
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 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.
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.
problem Inaccurate one-step-ahead forecasting limits stock market decision-making.
method Two novel methods: DCT-MFRFNN and VMD-MFRFNN.
result VMD-MFRFNN outperforms other methods in multi-step-ahead stock price prediction.
BCI provides calibrated prediction intervals for time series forecasts.
problem Calibration of prediction intervals for time series forecasts.
method BCI wraps around any time series forecasting models and optimizes interval lengths using dynamic programming.
result BCI achieves long-term coverage under arbitrary distribution shifts and temporal dependence.
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.
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.
A new framework evaluates deep learning vs classical forecasting methods for time series predictions.
problem Current forecasting model evaluation metrics fail to capture model performance differences.
method Proposes a novel framework for evaluating univariate time series forecasting models from multiple perspectives.
result Deep learning models like NHITS outperform classical methods in multi-step ahead forecasting but not in anomaly handling.
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.
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.
problem Analyzing the impact of ECB's unconventional policies on stock market volatility.
method Developed MEM with Asymmetry and Policy effects (MAP) models to separate base volatility from policy effects.
result Significant improvement in forecasting power after Expanded Asset Purchase Programme implementation.
Deep state space model forecasts time series with uncertainty.
problem Probabilistic forecasting for risk management.
method Parameterized deep networks for non-linear models, recurrent neural nets for dependency, ARD network for exogenous variables.
result Accurate and sharp probabilistic forecasts with realistic uncertainty growth.
Stanza: A Nonlinear State Space Model for Probabilistic Inference in Non-Stationary Time Seriesstat.ML
Stanza models complex time series with balance between traditional and deep learning approaches.
problem Capturing long-term structure in non-stationary time series.
method Nonlinear, non-stationary state space model.
result Achieves forecasting accuracy competitive with deep LSTMs, especially for multi-step ahead forecasting.
New methods for scoring function decomposition improve forecast evaluation.
problem Improving forecast evaluation and understanding forecast components.
method Linear recalibration of forecasts for miscalibration, discrimination, and uncertainty.
result Enhanced statistical power and deeper insights into forecast components.
MAGMA uses a common mean process to improve multi-step-ahead time series forecasting.
problem Improving multiple-step-ahead predictions for time series data.
method Proposes a novel multi-task Gaussian process framework with a common mean process for sharing information across tasks.
result Significantly improves predictive performances, even far from observations, and reduces computational complexity.
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…
ModelRadar evaluates forecasting models across multiple aspects.
problem Evaluating forecasting models using single scores hides relevant performance variations.
method ModelRadar, a framework for aspect-based evaluation of univariate time series forecasting models.
result NHITS performs best overall but its superiority varies with forecasting conditions.
ESM-CNN uses error feedback to build a random CNN for time series forecasting.
problem Improving time series forecasting accuracy with CNNs.
method Incrementally adding random filters and neurons to adaptively construct a CNN.
result ESM-CNN outperforms state-of-the-art models in prediction accuracy and efficiency.
Production forecasting is a key step to design the future development of a reservoir. A classical way to generate such forecasts consists in simulating future production for numerical models representative of the reservoir. However, identifying such models can be very challenging as they need to be constrained to all a…
Proposes a new model for more accurate demand forecasting considering dynamic contextual information.
problem Traditional methods fail to capture spatio-temporal and dynamic contextual dependencies in demand forecasting.
method Integrates temporal, relational, spatial, and dynamic contextual dependencies using a Context Integrated Graph Neural Network (CIGNN).
result CIGNN outperforms state-of-the-art baselines in multi-step ahead demand forecasting.
Study evaluates deep learning models for cryptocurrency price prediction.
problem Accurate cryptocurrency price forecasting models are needed due to market volatility.
method Reviewed and evaluated deep learning models including LSTM, CNN, and Transformer.
result Convolutional LSTM with multivariate approach provides best prediction accuracy.
MSCT predicts post-crash traffic speed using causal inference.
problem Time-varying confounding bias in post-crash traffic prediction.
method Marginal Structural Causal Transformer (MSCT) incorporating Marginal Structural Models and balanced loss function.
result MSCT outperforms state-of-the-art models in multi-step-ahead prediction.