FR \(^3\) LS: A Forecasting Model with Robust and Reduced Redundancy Latent Series

While some methods are confined to linear embeddings and others exhibit limited robustness, high-dimensional time series factorization techniques employ scalable matrix factorization for forecasting in latent space. This paper introduces a novel factorization method that employs a non-contrastive approach, guiding an autoencoder-like architecture to extract robust latent series while minimizing redundant information within the embeddings. The resulting learned representations are utilized by a temporal forecasting model, generating forecasts within the latent space, which are subsequently decoded back to the original space through the decoder. Extensive experiments demonstrate that our model achieves state-of-te-art performance on numerous commonly used datasets.