FutureMetrics: Using Deep Learning to Create a Multivariate Time Series Forecasting Platform

Liquidity forecasting is one of the most essential activities at any bank. TD bank, the largest of the big Five, has to provide liquidity for half a trillion dollars in products, and to forecast it to remain within a $5BN buffer. The use case was to predict liquidity growth over short to moderate time horizons: 90 days to 18 months. Model must perform reliably in a strict regulatory framework, and as such validating such a model to the required standards is a key area of focus for this talk. While univariate models are widely used for this reason, their performance is capped preventing future improvements for these type problems. The most challenging aspect of this problem is that the data is shallow (P N): the primary cadence is monthly, and chaotic nature of economic systems results in poor connectivity of behavior across transitions. Goal is to create an MLOps platform for these types of time series forecasting metrics across the enterprise. Connect with us: Website: Facebook: Twitter: LinkedIn: Instagram: