The autoregressive metric between ARIMA processes has been originally introduced as the Euclidean distance between the AR weights of the one-step-ahead forecasting functions. This article proposes a novel distance criterion between time series that compares the corresponding multistep ahead forecasting functions and that relies on the direct method for model estimation. The proposed approach is complemented by a strategy for visual exploration and clustering based on the DISTATIS algorithm.
Comparing multistep ahead forecasting functions for time series clustering / Corduas, M.; Ragozini, G.. - 1:215879(2018), pp. 191-199. (Intervento presentato al convegno 10th Scientific Meeting of the Classification and Data Analysis Group of the Italian Statistical Society, CLADAG 2015 tenutosi a ita nel 2015) [10.1007/978-3-319-55708-3_21].
Comparing multistep ahead forecasting functions for time series clustering
CORDUAS M.;RAGOZINI G.
2018
Abstract
The autoregressive metric between ARIMA processes has been originally introduced as the Euclidean distance between the AR weights of the one-step-ahead forecasting functions. This article proposes a novel distance criterion between time series that compares the corresponding multistep ahead forecasting functions and that relies on the direct method for model estimation. The proposed approach is complemented by a strategy for visual exploration and clustering based on the DISTATIS algorithm.I documenti in IRIS sono protetti da copyright e tutti i diritti sono riservati, salvo diversa indicazione.
