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 Plus annual

Presents the access of premium content as premium feature

Premium Content

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 Plus monthly

Global: Zendy Tools annual

Global: Zendy Tools monthly

Global: Zendy Free Plan

We extend recurrent neural networks to include several flexible timescalesfor each dimension of their output, which mechanically improves their abilitiesto account for processes with long memory or with highly disparate time scales.We compare the ability of vanilla and extended long short term memory networks(LSTMs) to predict asset price volatility, known to have a long memory.Generally, the number of epochs needed to train extended LSTMs is divided bytwo, while the variation of validation and test losses among models with thesame hyperparameters is much smaller. We also show that the model with thesmallest validation loss systemically outperforms rough volatility predictionsby about 20% when trained and tested on a dataset with multiple time series.

Recurrent Neural Networks with more flexible memory: better predictions than rough volatility