88 citations · 126 across the 18 of their papers we have counts for
18 papers
Are Data Embeddings effective in time series forecasting?
Reza Nematirad, Anil Pahwa, Balasubramaniam Natarajan
Time series forecasting plays a crucial role in many real-world applications, and numerous complex forecasting models have been proposed in recent years. Despite their architectura…
Times2D: Multi-Period Decomposition and Derivative Mapping for General Time Series Forecasting
Reza Nematirad, Anil Pahwa, Balasubramaniam Natarajan
Time series forecasting is an important application in various domains such as energy management, traffic planning, financial markets, meteorology, and medicine. However, real-time…
GNN-Based Candidate Node Predictor for Influence Maximization in Temporal Graphs
Priyanka Gautam, Balasubramaniam Natarajan, Sai Munikoti +2
In an age where information spreads rapidly across social media, effectively identifying influential nodes in dynamic networks is critical. Traditional influence maximization strat…
SPDNet: Seasonal-Periodic Decomposition Network for Advanced Residential Demand Forecasting
Reza Nematirad, Anil Pahwa, Balasubramaniam Natarajan
Residential electricity demand forecasting is critical for efficient energy management and grid stability. Accurate predictions enable utility companies to optimize planning and op…
Autoencoder-Based Domain Learning for Semantic Communication with Conceptual Spaces
Dylan Wheeler, Balasubramaniam Natarajan
Communication with the goal of accurately conveying meaning, rather than accurately transmitting symbols, has become an area of growing interest. This paradigm, termed semantic com…
Active Foundational Models for Fault Diagnosis of Electrical Motors
Sriram Anbalagan, Sai Shashank GP, Deepesh Agarwal +2
Fault detection and diagnosis of electrical motors are of utmost importance in ensuring the safe and reliable operation of several industrial systems. Detection and diagnosis of fa…