An Intelligent Technique for Forecasting Spatially Correlated Time Series

Abstract

The analysis of spatial autocorrelation has defined a new paradigm in ecology. Attention to spatial pattern leads to insights that would otherwise overlooked, while ignoring space may lead to false conclusions about ecological relationships. In this paper, we propose an intelligent forecasting technique, which explicitly accounts for the property of spatial autocorrelation when learning linear autoregressive models (ARIMA) of spatial correlated ecologic time series. The forecasting algorithm makes use of an autoregressive statistical technique, which achieves accurate forecasts of future data by taking into account temporal and spatial dimension of ecologic data. It uses a novel spatial-aware inference procedure, which permits to learn the autoregressive model by processing a time series in a neighborhood (spatial lags). Parameters of forecasting models are jointly learned on spatial lags of time series. Experiments with ecologic data investigate the accuracy of the proposed spatial-aware forecasting model with respect to the traditional one.