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Found 9 papers, 5 papers with code
iTransformer: Inverted Transformers Are Effective for Time Series Forecasting
These forecasters leverage Transformers to model the global dependencies over temporal tokens of time series, with each token formed by multiple variates of the same timestamp.
Continuous Invariance Learning
To address this challenge, we then propose Continuous Invariance Learning (CIL), which extracts invariant features across continuously indexed domains.
Time-LLM: Time Series Forecasting by Reprogramming Large Language Models
We begin by reprogramming the input time series with text prototypes before feeding it into the frozen LLM to align the two modalities.
DiffusionTrack: Diffusion Model For Multi-Object Tracking
In inference, the model refines a set of paired randomly generated boxes to the detection and tracking results in a flexible one-step or multi-step denoising diffusion process.
SLOTH: Structured Learning and Task-based Optimization for Time Series Forecasting on Hierarchies
Moreover, unlike most previous reconciliation methods which either rely on strong assumptions or focus on coherent constraints only, we utilize deep neural optimization networks, which not only achieve coherency without any assumptions, but also allow more flexible and realistic constraints to achieve task-based targets, e. g., lower under-estimation penalty and meaningful decision-making loss to facilitate the subsequent downstream tasks.
End-to-End Modeling Hierarchical Time Series Using Autoregressive Transformer and Conditional Normalizing Flow based Reconciliation
Multivariate time series forecasting with hierarchical structure is pervasive in real-world applications, demanding not only predicting each level of the hierarchy, but also reconciling all forecasts to ensure coherency, i. e., the forecasts should satisfy the hierarchical aggregation constraints.
A Graph Regularized Point Process Model For Event Propagation Sequence
Point process is the dominant paradigm for modeling event sequences occurring at irregular intervals.
A Meta Reinforcement Learning Approach for Predictive Autoscaling in the Cloud
Predictive autoscaling (autoscaling with workload forecasting) is an important mechanism that supports autonomous adjustment of computing resources in accordance with fluctuating workload demands in the Cloud.
A Riemannian Primal-dual Algorithm Based on Proximal Operator and its Application in Metric Learning
In this paper, we consider optimizing a smooth, convex, lower semicontinuous function in Riemannian space with constraints.
