The base level constant that shifts the series.
The autoregressive coefficient determining the influence of the previous value on the current value.
The number of samples to generate in the autoregressive process.
result: A double array containing n samples of the generated autoregressive process.
Notes: - The process is defined as: yi = alpha + rho * y[i-1] + noisei where noise[i] is drawn from a uniform distribution in the range [-1.0, 1.0]. - If rho = 0, the process degenerates into white noise. - If alpha = 0, the process remains centered around zero unless rho > 1, which would make it non-stationary. - Uses Random(unpredictableSeed) for randomness, ensuring different sequences in each execution. - The function dynamically appends values to time_series, which may cause memory reallocation. If performance is critical, preallocating the array may be more efficient. - AR models are commonly used in time series forecasting and financial modeling to capture short-term dependencies in data.
Summary: Generates an AutoRegressive (AR) process of order 1 (AR(1)). The function models a time series where each value is influenced by the previous value, a deterministic term (alpha), and a random noise component.