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A Machine Learning Approach to Biodiversity Time Series Analysis

EasyChair Preprint no. 2106

7 pagesDate: December 8, 2019


In this paper we have accessed the ecological changes of resources through time. A brief concept of time series and a case study of the observation of dragon flies in Kerala region have been studied. A machine learning approach of processing the time series data, some forecasting results like migration location, population growth, future presence on a particular dragonfly species and the prediction approach is highlighted in this paper.

Keyphrases: Biodiversity, Biodiversity time series, conservation, discrete functional variability time, Discrete functional variability time series, Discrete transitive auto-regressive model, Dragonfly distribution, Dragonfly migration, Entomology, irregular time series, LSTM encoder-decoder-predictor, open data

BibTeX entry
BibTeX does not have the right entry for preprints. This is a hack for producing the correct reference:
  author = {Rajarshi Paul and Th. Shanta Kumar},
  title = {A Machine Learning Approach to Biodiversity Time Series Analysis},
  howpublished = {EasyChair Preprint no. 2106},

  year = {EasyChair, 2019}}
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