Assessment of autoregressive integrated moving average (ARIMA), generalized linear autoregressive moving average (GLARMA), and random forest (RF) time series regression models for predicting influenza A virus frequency in swine in Ontario, Canada
نویسندگان
چکیده
منابع مشابه
Stationarity of Generalized Autoregressive Moving Average Models
Time series models are often constructed by combining nonstationary effects such as trends with stochastic processes that are believed to be stationary. Although stationarity of the underlying process is typically crucial to ensure desirable properties or even validity of statistical estimators, there are numerous time series models for which this stationarity is not yet proven. A major barrier...
متن کاملComparison of autoregressive integrated moving average (ARIMA) model and adaptive neuro-fuzzy inference system (ANFIS) model
Proper models for prediction of time series data can be an advantage in making important decisions. In this study, we tried with the comparison between one of the most useful classic models of economic evaluation, auto-regressive integrated moving average model and one of the most useful artificial intelligence models, adaptive neuro-fuzzy inference system (ANFIS), investigate modeling procedur...
متن کاملRank-Based Estimation for Autoregressive Moving Average Time Series Models
We establish asymptotic normality and consistency for rank-based estimators of autoregressive-moving average model parameters. The estimators are obtained by minimizing a rank-based residual dispersion function similar to the one given in L.A. Jaeckel [Estimating regression coefficients by minimizing the dispersion of the residuals, Ann. Math. Statist. 43 (1972) 1449–1458]. These estimators can...
متن کاملCensored Time Series Analysis with Autoregressive Moving Average Models
Time series measurements are often observed with data irregularities, such as censoring due to a detection limit. Practitioners commonly disregard censored data cases which often result into biased estimates. We present an attractive remedy for handling autocorrelated censored data based on a class of autoregressive and moving average (ARMA) models. In particular, we introduce an imputation met...
متن کاملForecasting Inflation: Autoregressive Integrated Moving Average Model
This study compares the forecasting performance of various Autoregressive integrated moving average (ARIMA) models by using time series data. Primarily, The Box-Jenkins approach is considered here for forecasting. For empirical analysis, we used CPI as a proxy for inflation and employed quarterly data from 1970 to 2006 for Pakistan. The study classified two important models for forecasting out ...
متن کاملذخیره در منابع من
با ذخیره ی این منبع در منابع من، دسترسی به آن را برای استفاده های بعدی آسان تر کنید
ژورنال
عنوان ژورنال: PLOS ONE
سال: 2018
ISSN: 1932-6203
DOI: 10.1371/journal.pone.0198313