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  1. Nicole Seaman

    P1.T2.20.25 Forecasting ARMA models

    Learning objectives: Explain how forecasts are generated from ARMA models. Describe the role of mean reversion in long-horizon forecasts. Explain how seasonality is modeled in a covariance-stationary ARMA. Questions: 20.25.1. Below is plotted the monthly growth rate of a new cryptocurrency...
  2. Nicole Seaman

    P1.T2.20.21. Stationary Time Series: covariance stationary, autocorrelation function (ACF) and white noise

    Learning objectives: Describe the requirements for a series to be covariance stationary. Define the autocovariance function and the autocorrelation function. Define white noise; describe independent white noise and normal (Gaussian) white noise. Question: 20.21.1. Pamela has been tasked to...