2 citations · 6 across the 7 of their papers we have counts for
3 papers · 1 filter
Learning Temporal Saliency for Time Series Forecasting with Cross-Scale Attention
Ibrahim Delibasoglu, Fredrik Heintz
Explainability in time series forecasting is essential for improving model transparency and supporting informed decision-making. In this work, we present CrossScaleNet, an innovati…
Scaling Transformers for Time Series Forecasting: Do Pretrained Large Models Outperform Small-Scale Alternatives?
Sanjay Chakraborty, Ibrahim Delibasoglu, Fredrik Heintz
Large pre-trained models have demonstrated remarkable capabilities across domains, but their effectiveness in time series forecasting remains understudied. This work empirically ex…
LMS-AutoTSF: Learnable Multi-Scale Decomposition and Integrated Autocorrelation for Time Series Forecasting
Ibrahim Delibasoglu, Sanjay Chakraborty, Fredrik Heintz
Time series forecasting is an important challenge with significant applications in areas such as weather prediction, stock market analysis, scientific simulations and industrial pr…