Time Series Forecasting with the Temporal Fusion Transformer

In Time Series Forecasting, deep learning neural networks have just lately outperformed conventional techniques and have done so by a smaller margin than in image and language processing. The days of creating a model specifically for a single time series, whether it be multivariate or univariate, are long gone. Nowadays, time series could be multivariate, have various distributions, and include more exploratory factors. The usual suspects, such as missing data, trends, seasonality, volatility, drift, and rare events, should also not be overlooked. A straightforward target variable prediction is frequently insufficient.