2 citations · 2 across the 5 of their papers we have counts for
6 papers
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…
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…
Channel Dependence, Limited Lookback Windows, and the Simplicity of Datasets: How Biased is Time Series Forecasting?
Ibram Abdelmalak, Kiran Madhusudhanan, Jungmin Choi +4
In Long-term Time Series Forecasting (LTSF), the lookback window is a critical hyperparameter often set arbitrarily, undermining the validity of model evaluations. We argue that th…
Wave-Mask/Mix: Exploring Wavelet-Based Augmentations for Time Series Forecasting
Dona Arabi, Jafar Bakhshaliyev, Ayse Coskuner +2
Data augmentation is important for improving machine learning model performance when faced with limited real-world data. In time series forecasting (TSF), where accurate prediction…
A.I. and Data-Driven Mobility at Volkswagen Financial Services AG
Shayan Jawed, Mofassir ul Islam Arif, Ahmed Rashed +8
Machine learning is being widely adapted in industrial applications owing to the capabilities of commercially available hardware and rapidly advancing research. Volkswagen Financia…
Multimodal Meta-Learning for Time Series Regression
Sebastian Pineda Arango, Felix Heinrich, Kiran Madhusudhanan +1
Recent work has shown the efficiency of deep learning models such as Fully Convolutional Networks (FCN) or Recurrent Neural Networks (RNN) to deal with Time Series Regression (TSR)…