Wow that title is a mouthful… But it’s not a complicated as it seems. Let’s break it down:
- Multi-Variate Time-Series Forecasts – Single-variate time-series forecasting uses only the historical values of the data in which we are attempting to predict future values. (For example, expontial decay, moving average, auto-regressive moving average.) Multi-Variate allows additional time-series and non-time-series variable to be including in the model to enhance the models predictive capability and give better understanding as to what influences our target predicted value(s). (For example, including the weighted seven day moving average sentiment of news articles about a company when forecasting it’s stock price for tomorrow.)
- Multi-Horizon Time-Series Forecasts – Traditional time series forecasting is typically optimized for a specified number of period ahead (for example, a produce department predicting next week’s potato sales to determine inventory). Multi Horizon means we attempt to predict many different future periods within in the same model. (For example, predicting daily potato sales for every day over the next four weeks to reduce the number of orders and schedule times for restocking.)
- Interpreting Patterns – A good model doesn’t only provide an accurate prediction, it also gives insights as to what inputs are driving the results, that is, the model is interpretable.
- Temporal Fusion Transformer – The name of the proposed Multi-Horizon Time-Series Forecasting framework. It combines elements of Long-Short Term Memory (LSTM) Convolutional Neural Networks (CNNs) and a mechanism first used in image recognition called “Attention” (We’ll talk more about attention later).