Semester of Graduation
First Major Professor
Master of Science (MS)
The objective of this creative component is to learn the ARTFIMA time series model and use it to fit real world data and compare the model with ARFIMA model in terms of goodness of fit and predictions. The ARTFIMA model is a short range dependent time series that exhibits semi-long range dependency. For small values of tempering parameter λ > 0, the spectral density behaves like a power law at low frequencies, and it remains bounded as frequency reaches to zero. ARTFIMA can be extended to Autoregressive Fractionally Integrated Moving Average (ARFIMA) when the tempered paramater λ is zero, and to Autoregressive Moving Average (ARMA, which is most commonly used time series model) when both tempered parameter λ and difference parameter d are zero. We can consider ARTFIMA model as a generalized time series model, where as both ARFIMA and ARMA are the extensions of ARTFIMA.
Dinesh Reddy Poddaturi
Poddaturi, Dinesh Reddy, "Autoregressive Tempered Fractionally Integrated Moving Average Model" (2018). Creative Components. 95.