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An eigen-based approach for complex-valued Forecasting

Forecasting one step ahead is generally straightforward. Forecasting two steps ahead a little more challenging. Forecasting further into the horizon may require prior forecasted samples, as the availability of historical data may not be adequate to do so. It is in this motivational context that we proposed an eigen-based approach for complex-valued multiple-step ahead forecasting. Here we establish an augmented complex-domain singular spectrum analysis framework to perform prediction beyond 50 step ahead. It is shown that other prediction algorithms such as the least mean square, though useful and adaptive, cannot use the predicted samples to predict further. In some cases, they may diverge from the trend. Simulations on real-world data support our approach.