Vijaya Krishna Yalavarthi

Most-Recent Anchoring with Recurrent Ordering for Time Series Forecasting

Jung Min Choi, Ngoc Son Le, Ibram Abdelmalak +2

Long-term forecasting models commonly process all patches in a look-back window using the same fixed stack. Older contextual patches and recent evidence therefore receive the same…

Dual-Context Analog Retrieval for Time Series Forecasting

Jung Min Choi, Ngoc Son Le, Ibram Abdelmalak +2

Most long-term time-series forecasting models map the look-back window directly to the full horizon in a single pass. While efficient, this design does not explicitly identify whic…

cs.AI2026

MixBench-TS: A Multivariate Time Series Forecasting Benchmark Where Channel Mixing Pays Off

Ibram Abdelmalak, Mischa Putzke, Jungmin Choi +3

Multivariate Time Series Forecasting (MTSF) models that mix information across channels assume that the past of one channel carries information about the future of another. Yet the…

Two-stage Odd Residual Flows for Mean-Preserving Probabilistic Time Series Forecasting

Kiran Madhusudhanan, Christian Klötergens, Lars Schmidt-Thieme +1

Probabilistic forecasting plays an essential role in risk-sensitive decision-making, particularly in long-horizon settings. However, existing approaches often face a fundamental tr…

Do Tabular Foundation Models Agree with Themselves?

Christian Klötergens, Vijaya Krishna Yalavarthi, Lars Schmidt-Thieme +1

Tabular Foundation Models (TFMs) are the state of the art for many tabular prediction tasks. They are transformers that approximate the Bayesian posterior predictive distribution u…

CAMP: A Cycle-Aware Multi-Scale Patch Mixer for Time Series Forecasting

Jung Min Choi, Vijaya Krishna yalavarthi, Lars Schmidt-Thieme

Real-world time series are often governed by recurring patterns, but their dominant periods may vary across datasets, forecasting settings, and individual input windows. Existing c…

The Importance of Encoder Choice:A Tabular-Image Study

Ilia Koloiarov, Diego Coello de Portugal Mecke, Vijaya Krishna Yalavarthi +2

Multimodal learning usually requires a dedicated encoder per modality. When a tabular modality is involved, prior work has been mostly using a \emph{plain MLP} as the encoder. Yet…

Gaussian Mixture Copula Processes for Irregular Time Series

Christian Klötergens, Tom Hanika, Lars Schmidt-Thieme +1

We introduce Gaussian Mixture Copula Processes (GMCP), a conditional copula process for irregularly sampled multivariate time series (IMTS) that is expressive and marginalization c…

Probabilistic Circuits for Irregular Multivariate Time Series Forecasting

Christian Klötergens, Vijaya Krishna Yalavarthi, Lars Schmidt-Thieme

Joint probabilistic modeling is essential for forecasting irregular multivariate time series (IMTS) to accurately quantify uncertainty. Existing approaches often struggle to balanc…

NPMixer: Hierarchical Neighboring Patch Mixing for Time Series Forecasting

Jung Min Choi, Vijaya Krishna Yalavarthi, Lars Schmidt-Thieme

Multivariate time series forecasting remains a challenge due to the complexity of local temporal dynamics and global dependencies across multiple variables. In this paper, we propo…

HPMixer: Hierarchical Patching for Multivariate Time Series Forecasting

Jung Min Choi, Vijaya Krishna Yalavarthi, Lars Schmidt-Thieme

In long-term multivariate time series forecasting, effectively capturing both periodic patterns and residual dynamics is essential. To address this within standard deep learning be…

TabResFlow: A Normalizing Spline Flow Model for Probabilistic Univariate Tabular Regression

Kiran Madhusudhanan, Vijaya Krishna Yalavarthi, Jonas Sonntag +2

Tabular regression is a well-studied problem with numerous industrial applications, yet most existing approaches focus on point estimation, often leading to overconfident predictio…

The Role of Active Learning in Modern Machine Learning

Thorben Werner, Lars Schmidt-Thieme, Vijaya Krishna Yalavarthi

Even though Active Learning (AL) is widely studied, it is rarely applied in contexts outside its own scientific literature. We posit that the reason for this is AL's high computati…

Mixing It Up: Exploring Mixer Networks for Irregular Multivariate Time Series Forecasting

Christian Klötergens, Tim Dernedde, Lars Schmidt-Thieme +1

Forecasting irregularly sampled multivariate time series with missing values (IMTS) is a fundamental challenge in domains such as healthcare, climate science, and biology. While re…

Physiome-ODE: A Benchmark for Irregularly Sampled Multivariate Time Series Forecasting Based on Biological ODEs

Christian Klötergens, Vijaya Krishna Yalavarthi, Randolf Scholz +3

State-of-the-art methods for forecasting irregularly sampled time series with missing values predominantly rely on just four datasets and a few small toy examples for evaluation. W…

Marginalization Consistent Probabilistic Forecasting of Irregular Time Series via Mixture of Separable flows

Vijaya Krishna Yalavarthi, Randolf Scholz, Christian Kloetergens +3

Probabilistic forecasting models for joint distributions of targets in irregular time series with missing values are a heavily under-researched area in machine learning, with, to t…

Functional Latent Dynamics for Irregularly Sampled Time Series Forecasting

Christian Klötergens, Vijaya Krishna Yalavarthi, Maximilian Stubbemann +1

Irregularly sampled time series with missing values are often observed in multiple real-world applications such as healthcare, climate and astronomy. They pose a significant challe…

Are EEG Sequences Time Series? EEG Classification with Time Series Models and Joint Subject Training

Johannes Burchert, Thorben Werner, Vijaya Krishna Yalavarthi +3

As with most other data domains, EEG data analysis relies on rich domain-specific preprocessing. Beyond such preprocessing, machine learners would hope to deal with such data as wi…