A spatio-temporal LSTM model to forecast across multiple temporal and spatial scales

TL;DR

This paper introduces a novel spatio-temporal LSTM model for environmental time series forecasting, effectively handling data sparsity and capturing complex spatial-temporal dependencies with a simple, scalable approach.

Contribution

The paper presents a new spatio-temporal LSTM architecture that models both spatial and temporal dependencies simultaneously, improving environmental data forecasting.

Findings

Accurately replicates complex oceanic signals.

Provides comparable performance to state-of-the-art benchmarks.

Offers a simpler, scalable training pipeline with missing data handling.

Abstract

This paper presents a novel spatio-temporal LSTM (SPATIAL) architecture for time series forecasting applied to environmental datasets. The framework was evaluated across multiple sensors and for three different oceanic variables: current speed, temperature, and dissolved oxygen. Network implementation proceeded in two directions that are nominally separated but connected as part of a natural environmental system -- across the spatial (between individual sensors) and temporal components of the sensor data. Data from four sensors sampling current speed, and eight measuring both temperature and dissolved oxygen evaluated the framework. Results were compared against RF and XGB baseline models that learned on the temporal signal of each sensor independently by extracting the date-time features together with the past history of data using sliding window matrix. Results demonstrated ability to accurately replicate complex signals and provide comparable performance to state-of-the-art benchmarks. Notably, the novel framework provided a simpler pre-processing and training pipeline that handles missing values via a simple masking layer. Enabling learning across the spatial and temporal directions, this paper addresses two fundamental challenges of ML applications to environmental science: 1) data sparsity and the challenges and costs of collecting measurements of environmental conditions such as ocean dynamics, and 2) environmental datasets are inherently connected in the spatial and temporal directions while classical ML approaches only consider one of these directions. Furthermore, sharing of parameters across all input steps makes SPATIAL a fast, scalable, and easily-parameterized forecasting framework.