A Machine Learning Approach to Biodiversity Time Series Analysis

EasyChair Preprint 2106

Abstract

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

@booklet{EasyChair:2106,
  author    = {Rajarshi Paul and Th. Shanta Kumar},
  title     = {A Machine Learning Approach to Biodiversity Time Series Analysis},
  howpublished = {EasyChair Preprint 2106},
  year      = {EasyChair, 2019}}