Stochastic Volatility Models with or without Leverage
c(person("Yasuhiro", "Omori", email = "omori.yasuhiro@gmail.com", role = c("aut", "cre")),person("Ryuji", "Hashimoto", role = "ctr"))
The efficient Markov chain Monte Carlo estimation of stochastic volatility models with and without leverage (asymmetric and symmetric stochastic volatility models). Further, it computes the logarithm of the likelihood given parameters using particle filters.
Rcpp (>= 1.0.7), freqdom, stats, graphics
Yasuhiro Omori [aut, cre],
Ryuji Hashimoto [ctr]

Automatic Time Series Analysis and Forecasting Using the Ata
Method
c(
person("Ali Sabri", "Taylan", email="alisabritaylan@gmail.com", role=c("aut", "cre", "cph"), comment = c(ORCID = "0000-0001-9514-934X")),
person("Hanife", "Taylan Selamlar", role=c("aut", "cph"), comment = c(ORCID = "0000-0002-4091-884X")),
person("Guckan", "Yapar", role=c("aut", "ths", "cph"), comment=c(ORCID = "0000-0002-0971-6676"))
)
The Ata method (Yapar et al. (2019) <doi:10.15672/hujms.461032>), an alternative to exponential smoothing (described in Yapar (2016) <doi:10.15672/HJMS.201614320580>,
Yapar et al. (2017) <doi:10.15672/HJMS.2017.493>), is a new univariate time series forecasting method which provides innovative solutions to issues faced during the
initialization and optimization stages of existing forecasting methods. Forecasting performance of the Ata method is superior to existing methods both in terms of easy
implementation and accurate forecasting. It can be applied to non-seasonal or seasonal time series which can be decomposed into four components (remainder, level, trend
and seasonal). This methodology performed well on the M3 and M4-competition data. This package was written based on Ali Sabri Taylan’s PhD dissertation.
graphics, forecast, Rcpp, Rdpack, seasonal, stats, stlplus,
stR, timeSeries, TSA, tseries, utils, xts
Ali Sabri Taylan [aut, cre, cph]
(<https://orcid.org/0000-0001-9514-934X>),
Hanife Taylan Selamlar [aut, cph]
(<https://orcid.org/0000-0002-4091-884X>),
Guckan Yapar [aut, ths, cph] (<https://orcid.org/0000-0002-0971-6676>)