  Forecasting of time series in continuous systems becomes an increasingly
relevant task due to recent developments in IoT and 5G. The popular forecasting
model ARIMA is applied to a large variety of applications for decades. An
online variant of ARIMA applies the Online Newton Step in order to learn the
underlying process of the time series. This optimization method has pitfalls
concerning the computational complexity and convergence. Thus, this work
focuses on the computational less expensive Online Gradient Descent
optimization method, which became popular for learning of neural networks in
recent years. For the iterative training of such models, we propose a new
approach combining different Online Gradient Descent learners (such as Adam,
AMSGrad, Adagrad, Nesterov) to achieve fast convergence. The evaluation on
synthetic data and experimental datasets show that the proposed approach
outperforms the existing methods resulting in an overall lower prediction
error.
