# Animal Movement Prediction Based on Predictive Recurrent Neural Network
## 2 Related Works  
To date, many researchers have attempted to figure out the pattern of animal movement using various machine learning technologies. For instance, [PERSON] et al. [18] proposed a meta-analysis method, which combines state-space models (SSM) and Bayesian approach for individual movement inference. They analyzed the relationship between leatherback turtle movement and environmental changes such as sea surface temperature. [PERSON] et al. [19] developed an extracting method of spatiotemporal patterns in animal trajectories. They conducted a tree-based approach to identify the pattern of habitat selection and visualize the exploring temporal pattern of habitat use through hierarchical trees. [PERSON] et al. [20] proposed a conceptual simulation model of animal personality which includes foraging search performance, habitat preference, home ranging utilization pattern, social network and animal population progress. They argued that considering personality-dependent movement can generate far-reaching predictions about the spatial pattern in ecology. [PERSON] [21] inferred animal behavior using machine learning methods such as state-space models, hidden Markov models, random forests, and support vector machines. Through various experiments, they confirmed that machine learning methods can be applied for quantifying the spatial and temporal distributions of specific behavior patterns.  
Predictions of long-short term animal movement and population shift have ecological importance because they are directly related to significant factors such as spatiotemporal changes of weather and habitat. Very few studies have predicted movement using long-short term tracking animal data. [PERSON] et al. [22] proposed a periodic movement analysis algorithm named 'Periodica'. They focused on finding periods in complex animal movement and developed an optimal method of periodic animal behaviors analysis. The algorithm contains Fourier transform, autocorrelation, and hierarchical clustering for retrieving periods and formulating the periodic behaviors. [PERSON] et al. [23] presented a simple recursion analysis model that simulates the movements of large-herbivore groups. They defined the movement pattern, which includes clockwise and counter-clockwise pattern and simply formulated the African buffalo trajectories using furrier transform. In 2010, [PERSON] et al. [24] developed spatiotemporal exploratory models (STEM) using broad-scale survey data. They focused on the population change of animal distribution and proposed a flexible framework for analyzing the dynamic pattern of species occurrence and abundance from broad-scale data. To construct STEM, they utilized an ensemble model of decision trees. Through various experiments, they emphasized that STEM is a useful analytical tool of animal exploratory. Three years later, [PERSON] et al. [25] presented an adaptive spatiotemporal exploratory model (AdaSTEM), which can exploit variation in the density of observations while adapting to multiple scales in space and time. The AdaSTEM utilized the crowdsourcing data of animal observation for building estimation model and provided an ecological insight that indicates population movement of long-range migration animals. Through AdaSTEM, they estimated the spatiotemporal distribution of Barn Swallow across the western hemisphere during the breeding season, fall migration and winter migration.  
To summarize, previous studies on movement prediction models have mainly focused on the behavior state changes such as 'breeding' to'migrating' or 'roaming' to'resting'. As far as we know, few works have been done to predict long-short term animal movement or population shift especially by using continuous tracking data to construct a reasonable prediction model. In this paper, we propose an animal movement prediction model to predict the aggregate movements of animals over short-long term periods by considering continuous tracking data of individuals which are effected by spatiotemporal changes such as weather and movement characteristics.