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

Transformers have demonstrated impressive strength in long-term seriesforecasting.  Existing prediction research mostly focused on mapping past short sub-series(lookback window) to future series (forecast window). The longer trainingdataset time series will be discarded, once training is completed. Models canmerely rely on lookback window information for inference, which impedes modelsfrom analyzing time series from a global perspective. And these windows used byTransformers are quite narrow because they must model each time-step therein.Under this point-wise processing style, broadening windows will rapidly exhausttheir model capacity. This, for fine-grained time series, leads to a bottleneckin information input and prediction output, which is mortal to long-term seriesforecasting. To overcome the barrier, we propose a brand-new methodology toutilize Transformer for time series forecasting. Specifically, we split timeseries into patches by day and reform point-wise to patch-wise processing,which considerably enhances the information input and output of Transformers.To further help models leverage the whole training set's global informationduring inference, we distill the information, store it in time representations,and replace series with time representations as the main modeling entities. Ourdesigned time-modeling Transformer -- Dateformer yields state-of-the-artaccuracy on 7 real-world datasets with a 33.6\% relative improvement andextends the maximum forecast range to half-year.

Dateformer: Time-modeling Transformer for Longer-term Series Forecasting