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 Tools annual

Global: Zendy Free Plan

Global: Zendy Tools monthly

Global: Zendy Plus monthly

In real-world time series recognition applications, it is possible to havedata with varying length patterns. However, when using artificial neuralnetworks (ANN), it is standard practice to use fixed-sized mini-batches. To dothis, time series data with varying lengths are typically normalized so thatall the patterns are the same length. Normally, this is done using zero paddingor truncation without much consideration. We propose a novel method ofnormalizing the lengths of the time series in a dataset by exploiting thedynamic matching ability of Dynamic Time Warping (DTW). In this way, the timeseries lengths in a dataset can be set to a fixed size while maintainingfeatures typical to the dataset. In the experiments, all 11 datasets withvarying length time series from the 2018 UCR Time Series Archive are used. Weevaluate the proposed method by comparing it with 18 other length normalizationmethods on a Convolutional Neural Network (CNN), a Long-Short Term Memorynetwork (LSTM), and a Bidirectional LSTM (BLSTM).

On Mini-Batch Training with Varying Length Time Series