English (United Kingdom)

https://curated-unify.zendy.io/wp-json/zendy-region/v1/featured_content/oa?rat=en

https://curated-unify.zendy.io/wp-json/zendy-region/v1/highlighted_journal/

Global: Zendy Tools annual

Presents the keyphrase highlighting as premium feature

Keyphrase Highlighting

Presents the summarisation as premium feature

Summarisation

Insights

Presents the pdf analysis as premium feature

PDF Analysis

Presents the zaia usage as premium feature

ZAIA

Global: Zendy Tools monthly

Global: Zendy Plus monthly

Presents the access of premium content as premium feature

Premium Content

Global: Zendy Plus annual

Global: Zendy Free Plan

Time series with long-term structure arise in a variety of contexts andcapturing this temporal structure is a critical challenge in time seriesanalysis for both inference and forecasting settings. Traditionally, statespace models have been successful in providing uncertainty estimates oftrajectories in the latent space. More recently, deep learning, attention-basedapproaches have achieved state of the art performance for sequence modeling,though often require large amounts of data and parameters to do so. We proposeStanza, a nonlinear, non-stationary state space model as an intermediateapproach to fill the gap between traditional models and modern deep learningapproaches for complex time series. Stanza strikes a balance betweencompetitive forecasting accuracy and probabilistic, interpretable inference forhighly structured time series. In particular, Stanza achieves forecastingaccuracy competitive with deep LSTMs on real-world datasets, especially formulti-step ahead forecasting.

Stanza: A Nonlinear State Space Model for Probabilistic Inference in Non-Stationary Time Series