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Syama Sundar Rangapuram

9 accepted papers

2025

ChronosX: Adapting Pretrained Time Series Models with Exogenous Variables

Sebastian Pineda Arango, Pedro Mercado, Shubham Kapoor, Abdul Fatir Ansari, Lorenzo Stella, Huibin Shen +7
AISTATS 2025poster

Covariates provide valuable information on external factors that influence time series and are critical in many real-world time series forecasting tasks. For example, in retail, covariates may indicate promotions or peak dates such as holiday seasons that heavily influence demand forecasts. Recent a…

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2025

Enhancing Foundation Models for Time Series Forecasting via Wavelet-based Tokenization

Luca Masserano, Abdul Fatir Ansari, Boran Han, Xiyuan Zhang, Christos Faloutsos, Michael W. Mahoney +5
ICML 2025poster

How to best develop foundational models for time series forecasting remains an important open question. Tokenization is a crucial consideration in this effort: what is an effective discrete vocabulary for a real-valued sequential input? To address this question, we develop WaveToken, a wavelet-based…

Cited by 2Source ↗PDF ↗Scholar ↗
2023

Coherent Probabilistic Forecasting of Temporal Hierarchies

Syama Sundar Rangapuram, Shubham Kapoor, Rajbir Singh Nirwan, Pedro Mercado, Tim Januschowski, Yuyang Wang +1
AISTATS 2023poster

Forecasts at different time granularities are required in practice for addressing various business problems starting from short-term operational to medium-term tactical and to long-term strategic planning. These forecasting problems are usually treated independently by learning different ML models w…

Cited by 20Source ↗PDF ↗Scholar ↗
2021

End-to-End Learning of Coherent Probabilistic Forecasts for Hierarchical Time Series

Syama Sundar Rangapuram, Lucien D Werner, Konstantinos Benidis, Pedro Mercado, Jan Gasthaus, Tim Januschowski
ICML 2021spotlight

This paper presents a novel approach for hierarchical time series forecasting that produces coherent, probabilistic forecasts without requiring any explicit post-processing reconciliation. Unlike the state-of-the-art, the proposed method simultaneously learns from all time series in the hierarchy an…

Cited by 91Source ↗PDF ↗Scholar ↗
2021

Neural Flows: Efficient Alternative to Neural ODEs

Marin Biloš, Johanna Sommer, Syama Sundar Rangapuram, Tim Januschowski, Stephan Günnemann
NeurIPS 2021poster

Neural ordinary differential equations describe how values change in time. This is the reason why they gained importance in modeling sequential data, especially when the observations are made at irregular intervals. In this paper we propose an alternative by directly modeling the solution curves - t…

Cited by 91Source ↗PDF ↗code ↗Scholar ↗
2020

Deep Rao-Blackwellised Particle Filters for Time Series Forecasting

Richard Kurle, Syama Sundar Rangapuram, Emmanuel de Bézenac, Stephan Günnemann, Jan Gasthaus
NeurIPS 2020poster

This work addresses efficient inference and learning in switching Gaussian linear dynamical systems using a Rao-Blackwellised particle filter and a corresponding Monte Carlo objective. To improve the forecasting capabilities, we extend this classical model by conditionally linear state-to-switch dyn…

Cited by 44Source ↗PDF ↗Scholar ↗
2020

Normalizing Kalman Filters for Multivariate Time Series Analysis

Emmanuel de Bézenac, Syama Sundar Rangapuram, Konstantinos Benidis, Michael Bohlke-Schneider, Richard Kurle, Lorenzo Stella +3
NeurIPS 2020poster

This paper tackles the modelling of large, complex and multivariate time series panels in a probabilistic setting. To this extent, we present a novel approach reconciling classical state space models with deep learning methods. By augmenting state space models with normalizing flows, we mitigate imp…

Cited by 155Source ↗PDF ↗Scholar ↗
2019

Probabilistic Forecasting with Spline Quantile Function RNNs

Jan Gasthaus, Konstantinos Benidis, Yuyang Wang, Syama Sundar Rangapuram, David Salinas, Valentin Flunkert +1
AISTATS 2019poster

In this paper, we propose a flexible method for probabilistic modeling with conditional quantile functions using monotonic regression splines. The shape of the spline is parameterized by a neural network whose parameters are learned by minimizing the continuous ranked probability score. Within this…

Cited by 216Source ↗PDF ↗Scholar ↗
2018

Deep State Space Models for Time Series Forecasting

Syama Sundar Rangapuram, Matthias W Seeger, Jan Gasthaus, Lorenzo Stella, Yuyang Wang, Tim Januschowski
NeurIPS 2018poster

We present a novel approach to probabilistic time series forecasting that combines state space models with deep learning. By parametrizing a per-time-series linear state space model with a jointly-learned recurrent neural network, our method retains desired properties of state space models such as d…

Cited by 980Source ↗PDF ↗Scholar ↗