IEEE

Latent Cluster Discovery in a Collection of Low Count Non-Gaussian Time Series in Economics Domain

Abstract: The main focus of this work is to discover naturally occurring clusters in behavioral time series, and then associate a numerical representation with every cluster, which could be used to ...
GitHub

Is Mamba Effective for Time Series Forecasting?

We propose S-Mamba, a Mamba-based model for time series forecasting, which delegates the extraction of inter-variate correlations and temporal dependencies to a bidirectional Mamba block and a ...
GitHub

Time Series Foundation Model - TEMPO: Prompt-based Generative Pre-trained Transformer for Time Series Forecasting

The official code for ["TEMPO: Prompt-based Generative Pre-trained Transformer for Time Series Forecasting (ICLR 2024)"]. TEMPO is one of the very first open source Time Series Foundation Models for ...
IEEE

Unsupervised Feature Selection-Based Interval Enhancement for Time Series Clustering and Its Application

Abstract: The proliferation of sensor-generated data necessitates robust time series clustering (TSCL) methods that effectively capture features and perceive multiscale patterns. Thus, in this article ...