ATMs fault prediction

The collected dataset consists of multivariate time series (MTS) data belonging to several ATMs banking along with the annotations that the operators did when they performed a maintenance task on any of the machines.

Each sample is a MTS with 144 points, associated with all the 10-minute time windows of that day and 38 dimensions. Each dimension is related to a command type and response type, where the value of each point in the time series represents the number of occurrences of the associated command and response since the last failure event., i.e. in this cycle of failure. Therefore, the time series value is accumulated until the next cycle begins. Additionally, some extra information is included for each sample, such as the cycle of failure and the machine identifier, which can be used to create data partitions without mixing different machines.

The dataset and the labels assigned to each sample was used in the original work to perform a binary classification problem addressed by ML techniques. The goal of the problem was to predict whether a failure will occur within the next 7 days, using only the information from the current day (accumulated since the last error), which is based on an event-log.

Potential use cases of the dataset: • multivariate time series classification-regression-forecasting methodologies; • feature learning- feature extraction approaches; • predictive maintenance tasks: failure classification, failure prediction, anomaly detection.