The autoregressive (AR) coefficient determining the influence of the previous value on the current value.
The moving average (MA) coefficient determining the influence of the previous noise term on the current value.
The number of samples to generate in the ARMA(1,1) process.
result: A double array containing n samples of the generated ARMA(1,1) process.
Notes: - The process is defined as: resulti = alpha * result[i-1] + beta * white_noise[i-1] + white_noisei where white_noise[i] is sampled from a uniform distribution in the range [-1.0, 1.0]. - Uses Random(unpredictableSeed) for randomness, ensuring different sequences in each execution. - The first value is initialized as a random value in the range [-1.0, 1.0]. - If alpha = 0, the process reduces to a Moving Average (MA) model. - If beta = 0, the process reduces to an AutoRegressive (AR) model. - The function dynamically appends values to result, which may cause memory reallocation. If performance is critical, preallocating the array may be more efficient. - ARMA models are widely used in financial modeling and time-series forecasting for capturing both short-term dependencies and noise effects.
Summary: Generates an AutoRegressive Moving Average (ARMA) process of order (1,1). The function simulates a time series where each value is influenced by both past values (autoregressive component) and white noise (moving average component).