Probabilistic Forecasting with Temporal Convolutional Neural Network
We present a probabilistic forecasting framework based on convolutional neural network for multiple related time series forecasting. The framework can be applied to estimate probability density under both parametric and non-parametric settings. More specifically, stacked residual blocks based on dilated causal convolutional nets are constructed to capture the temporal dependencies of the series. Combined with representation learning, our approach is able to learn complex patterns such as seasonality, holiday effects within and across series, and to leverage those patterns for more accurate forecasts, especially when historical data is sparse or unavailable. Extensive empirical studies are performed on several real-world datasets, including datasets from JD.com, China's largest online retailer. The results show that our framework outperforms other state-of-the-art methods in both accuracy and efficiency.
Code (5)
Tasks
Multivariate Time Series ForecastingProbabilistic Time Series ForecastingRepresentation LearningTime SeriesTime Series AnalysisTime Series ForecastingSimilar Papers 제목 키워드 기반
Temporal Convolutional Attention Neural Networks for Time Series Forecasting
Temporal Convolutional Neural Networks (TCNNs) have been applied for various sequence modelling tasks including time series forecasting. However, TCNNs may require many convolutional layers if the input sequence is long …
Multivariate Time Series ForecastingProbabilistic Time Series ForecastingTime SeriesTime Series Analysis+1Denoising Diffusion Probabilistic Models for Coastal Inundation Forecasting
Coastal flooding poses significant risks to communities, necessitating fast and accurate forecasting methods to mitigate potential damage. To approach this problem, we present DIFF-FLOOD, a probabilistic spatiotemporal f…
DenoisingA Multi-Horizon Quantile Recurrent Forecaster
We propose a framework for general probabilistic multi-step time series regression. Specifically, we exploit the expressiveness and temporal nature of Sequence-to-Sequence Neural Networks (e.g. recurrent and convolutiona…
quantile regressionregressionTime SeriesTime Series Analysis+1Probabilistic Dynamic Line Rating Forecasting with Line Graph Convolutional LSTM
Dynamic line rating (DLR) is a promising solution to increase the utilization of transmission lines by adjusting ratings based on real-time weather conditions. Accurate DLR forecast at the scheduling stage is thus necess…
SchedulingBack to square one: probabilistic trajectory forecasting without bells and whistles
We introduce a spatio-temporal convolutional neural network model for trajectory forecasting from visual sources. Applied in an auto-regressive way it provides an explicit probability distribution over continuations of a…
RelationTrajectory Forecasting