A Bayesian protocol identifies single- and two-source neutron configurations from spectra with greater than 4 sigma significance at event counts as low as 1000.
Deepar: Probabilistic forecasting with autoregressive recurrent networks.CoRR, abs/1704.04110
7 Pith papers cite this work. Polarity classification is still indexing.
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abstract
Probabilistic forecasting, i.e. estimating the probability distribution of a time series' future given its past, is a key enabler for optimizing business processes. In retail businesses, for example, forecasting demand is crucial for having the right inventory available at the right time at the right place. In this paper we propose DeepAR, a methodology for producing accurate probabilistic forecasts, based on training an auto regressive recurrent network model on a large number of related time series. We demonstrate how by applying deep learning techniques to forecasting, one can overcome many of the challenges faced by widely-used classical approaches to the problem. We show through extensive empirical evaluation on several real-world forecasting data sets accuracy improvements of around 15% compared to state-of-the-art methods.
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Logo-LLM improves time series forecasting by pulling local dynamics from shallow LLM layers and global trends from deeper layers, then aligning them via new Local-Mixer and Global-Mixer modules.
ARU embeds closed-form local linear models based on conditional Gaussian sufficient statistics into deep global forecasting networks for efficient streaming per-series adaptation.
ParaRNN decouples RNN dynamics into interpretable additive components, enabling parallelization and nonparametric regression bounds while matching vanilla RNN performance on sequential tasks.
ST-PT turns transformers into explicit factor graphs for time series, enabling structural injection of symbolic priors, per-sample conditional generation, and principled latent autoregressive forecasting via MFVI iterations.
MR-CDM uses hierarchical multi-resolution decomposition and multi-scale conditional diffusion to generate forecasts that reduce MAE and RMSE by 6-10% versus baselines like CSDI and Informer on four datasets.
Generalized ML models trained on past sales data forecast demand for new fashion items from their attributes, with experiments across neural architectures and loss functions showing robust performance.
citing papers explorer
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Identifying Neutron Sources using Recoil and Time-of-Flight Spectroscopy
A Bayesian protocol identifies single- and two-source neutron configurations from spectra with greater than 4 sigma significance at event counts as low as 1000.
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Logo-LLM: Local and Global Modeling with Large Language Models for Time Series Forecasting
Logo-LLM improves time series forecasting by pulling local dynamics from shallow LLM layers and global trends from deeper layers, then aligning them via new Local-Mixer and Global-Mixer modules.
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Streaming Adaptation of Deep Forecasting Models using Adaptive Recurrent Units
ARU embeds closed-form local linear models based on conditional Gaussian sufficient statistics into deep global forecasting networks for efficient streaming per-series adaptation.
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ParaRNN: An Interpretable and Parallelizable Recurrent Neural Network for Time-Dependent Data
ParaRNN decouples RNN dynamics into interpretable additive components, enabling parallelization and nonparametric regression bounds while matching vanilla RNN performance on sequential tasks.
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Exploring the Potential of Probabilistic Transformer for Time Series Modeling: A Report on the ST-PT Framework
ST-PT turns transformers into explicit factor graphs for time series, enabling structural injection of symbolic priors, per-sample conditional generation, and principled latent autoregressive forecasting via MFVI iterations.
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MR-ImagenTime: Multi-Resolution Time Series Generation through Dual Image Representations
MR-CDM uses hierarchical multi-resolution decomposition and multi-scale conditional diffusion to generate forecasts that reduce MAE and RMSE by 6-10% versus baselines like CSDI and Informer on four datasets.
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Fashion Retail: Forecasting Demand for New Items
Generalized ML models trained on past sales data forecast demand for new fashion items from their attributes, with experiments across neural architectures and loss functions showing robust performance.