Discovering the Arrow of Time in Machine Learning

Machine learning (ML) is increasingly useful as data grow in volume and accessibility. ML can perform tasks (e.g., categorisation, decision making, anomaly detection, etc.) through experience and without explicit instruction, even when the data are too vast, complex, highly variable, full of errors to be analysed in other ways. Thus, ML is great for natural language, images, or other complex and messy data available in large and growing volumes. Selecting ML models for tasks depends on many factors as they vary in supervision needed, tolerable error levels, and ability to account for order or temporal context, among many other things. Importantly, ML methods for tasks that use explicitly ordered or time-dependent data struggle with errors or data asymmetry. Most data are (implicitly) ordered or time-dependent, potentially allowing a hidden 'arrow of time' to affect ML performance on non-temporal tasks. This research explores the interaction of ML and implicit order using two ML models to automatically classify (a non-temporal task) tweets (temporal data) under conditions that balance volume and complexity of data. Results show that performance was affected, suggesting that researchers should carefully consider time when matching appropriate ML models to tasks, even when time is only implicitly included.