Hello! I'm working on a multivariate time series, 7 independent variable as rainfall depth (mm/hr) and one dependent variable as stream-flow (cumecs), so i want to develop a flood forecasting model by using past values of streamflow time series, together with joint time series of the observed present and past, as well as anticipated future values of rainfall time series. I want to forecast rainfall the from the forecasted rainfall I can be able to predict the stream flow.
Let y be the variable (scalar/vector-valued) to be forecasted let Yt be the joint time series of the
present and past values of y.
Yt = [yt, yt-1,…, yt-n],
Let u be the variable (scalar or vector-valued) that is in causal relationship with y, and let Ut be
the joint time-series of the observed present and past, as well as any anticipated future values
(denoted by hat) of u, such that,
Ut = [Ūt+α, ut, ut-1,…,ut-n], and let
Zt = [Yt, Ut],
α > 0,
Lead time forecast of the y variable is p(yt+α|Zt),
yt+1 = [yt, yt-1, …, yt-n; Ūt+1, ut, ut-1,…,ut-n]
Inwould like to use neural networks and svm to carry out this task