2 citations · 4 across the 11 of their papers we have counts for
12 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…
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…
Hyperparameter Tuning MLPs for Probabilistic Time Series Forecasting
Kiran Madhusudhanan, Shayan Jawed, Lars Schmidt-Thieme
Time series forecasting attempts to predict future events by analyzing past trends and patterns. Although well researched, certain critical aspects pertaining to the use of deep le…
ProbSAINT: Probabilistic Tabular Regression for Used Car Pricing
Kiran Madhusudhanan, Gunnar Behrens, Maximilian Stubbemann +1
Used car pricing is a critical aspect of the automotive industry, influenced by many economic factors and market dynamics. With the recent surge in online marketplaces and increase…
Forecasting Early with Meta Learning
Shayan Jawed, Kiran Madhusudhanan, Vijaya Krishna Yalavarthi +1
In the early observation period of a time series, there might be only a few historic observations available to learn a model. However, in cases where an existing prior set of datas…
Temporal Saliency Detection Towards Explainable Transformer-based Timeseries Forecasting
Nghia Duong-Trung, Duc-Manh Nguyen, Danh Le-Phuoc
Despite the notable advancements in numerous Transformer-based models, the task of long multi-horizon time series forecasting remains a persistent challenge, especially towards exp…
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…
Yformer: U-Net Inspired Transformer Architecture for Far Horizon Time Series Forecasting
Kiran Madhusudhanan, Johannes Burchert, Nghia Duong-Trung +2
Time series data is ubiquitous in research as well as in a wide variety of industrial applications. Effectively analyzing the available historical data and providing insights into…
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)…