arXiv:2606. 17996v1 Announce Type: cross Abstract: Cyclicity and trend are important components of time series data and many studies based on cyclicity and trend have achieved good results in long-term time series forecasting.
Summary generated by The Flow from the publisher's feed. The full article lives at arXiv AI.
arXiv:2604. 05543v2 Announce Type: replace Abstract: Multivariate time series forecasting often struggles to capture long-range dependencies due to fixed lookback windows.
arXiv:2608. 08788v1 Announce Type: cross Abstract: Koopman theory offers a linear-operator view of nonlinear sequence dynamics by lifting observations into a space where evolution is governed by a linear time-invariant Koopman operator.
arXiv:2605. 07476v2 Announce Type: replace Abstract: Multivariate time series forecasting remains a challenge due to the complexity of local temporal dynamics and global dependencies across multiple variables.
arXiv:2605. 15690v2 Announce Type: replace Abstract: Accurate and efficient long-term multivariate time series forecasting requires capturing recurring temporal structure while keeping inference cheap across many variables and horizons.
arXiv:2606. 27908v1 Announce Type: new Abstract: Long-term time series forecasting finds extensive applications in domains such as power demand, traffic flow, meteorological observation, and renewable energy dispatch.
arXiv:2602. 01588v3 Announce Type: replace-cross Abstract: Multimodal time series forecasting is crucial in real-world applications, where decisions depend on both numerical data and contextual signals.