What is Multivariate forecasting?
Multivariate Forecasting is a statistical technique to employ future values for multiple interconnected variables simultaneously. The process of multivariate forecasting begins by collecting historical data for all the features and then these datasets are analyzed to identify patterns, correlations based on relationships, and predict the future values.
Common techniques utilized in multivariate forecasting include Vector Autoregression (VAR), which models the interdependencies between multiple time series variables, and structural equation modeling (SEM), which allows for the examination of complex relationships between variables. Moreover, machine learning algorithms like neural networks and gradient boosting machines have also been increasingly employed in multivariate forecasting tasks due to their ability to capture intricate patterns and nonlinear relationships within data.
Multivariate Time Series Forecasting with LSTMs in Keras
Multivariate forecasting entails utilizing multiple time-dependent variables to generate predictions. This forecasting approach incorporates historical data while accounting for the interdependencies among the variables within the model. In this article, we will explore the world of multivariate forecasting using LSTMs, peeling back the layers to understand its core, explore its applications, and grasp the revolutionary influence it has on steering decision-making towards the future.