Multivariate time series forecasting has long received significant attention in real-world applications, such as energy consumption and traffic prediction. While recent methods demonstrate good abilities, they have three fundamental limitations. (i) Discrete neural architectures: Interlacing individually parameterized spatial temporal blocks to encode rich underlying patterns leads discontinuou...

Demand for mineral resources is increasing, necessitating exploitation of lower grade and more heterogeneous orebodies. The high variability inherent in such orebodies leads to an increase the cost, complexity environmental footprint associated with mining processing. Enhanced knowledge orebody characteristics thus vital companies optimize profitability. We present a pilot study investigate pre...

Unsupervised Domain Adaptation (UDA) methods can reduce label dependency by mitigating the feature discrepancy between labeled samples in a source domain and unlabeled similar yet shifted target domain. Though achieving good performance, these are inapplicable for Multivariate Time-Series (MTS) data. MTS data collected from multiple sensors, each of which follows various distributions. However,...