Earthquake prediction is the field of seismology concerned with predicting the time, location, and magnitude of earthquakes within a small time frame, usually defined in terms of minutes or seconds before an event. Such predictions can have a large impact on minimizing the damage caused by these seismic events, by providing early warnings to the affected population and allowing them to respond appropriately. Although the methods used to predict earthquakes are often limited to larger magnitude events, predicting micro-earthquakes is also an important task, especially in locations that are more vulnerable to seismic shocks. Concentrations of localized micro-earthquakes can also hint at larger future seismic events, and their location can be used to locate moving fault lines underground. Deep learning methods perform particularly well in this context, due to their ability to quickly identify patterns in large volumes of data, and Long-Short Term Memory (LSTM) neural networks are very well suited to handling time-sequenced data such as the seismic waves used in earthquake prediction problems. In this paper, an LSTM network is trained to predict micro-earthquakes three seconds before the event, using seismic recordings from the New Zealand dataset: the goal is to find the optimal size of these recordings and understand how different values affect the model. Our results suggest that larger recordings do not provide any benefit in performance and that high levels of accuracy can be reached with smaller samples. This means that in the context of micro-earthquake prediction, primary waves can be easily detected in short recordings, and are likely to travel close to shear waves. This can be attributed to the low strength of signals that micro-earthquakes generate, as they will travel shorter distances than major earthquakes.