Friday, June 07, 2024
Parsee Mizuhashi in Touhou Project with A wedding dress costume
Tuesday, June 04, 2024
A wedding dress costume of Dai-Tengu Megumu Iizunamaru for the June bride season!
Sunday, June 02, 2024
The scene Aya Shameimaru is calling Daitengu Megumu Iizunamaru in The Memories of Phantasm, Touhou Project
Touhou Project, The Memories of Phantasm, Episode 14, Aya Shameimaru contacting her Daitengu boss: This Daitengu boss must be Megumu Iizunamaru!
* Voice of Megumu Iizunamaru is Yukkuri voice *
This illustration is a redrawn version by ChatGPT, inspired by the original artwork found at
My pixiv page: https://www.pixiv.net/en/artworks/119267886
Sunday, May 26, 2024
Council of Five Elders, Touhou Project
Thursday, May 23, 2024
ARCH, GARCH, and ARIMA
Auto-Regressive Conditional Heteroscedasticity (ARCH)
ARCH was developed by an economist Robert F. Engle III having won the 2003 Nobel Memorial Prize in Economic Sciences for its achievement.
Generalised Auto-Regressive Conditional Heteroscedasticity (GARCH)
GARCH assumes the variance of the error term symmetrically varies depending the average size of the error terms in pervious time steps. It adds the lagged variance on the explanatory variable of the second regression with reference to the log-likelihood for finding the coefficients γ and δ:
Auto-Regressive Integrated Moving-Average (ARIMA)
Another popular auto-regressive method (AR) + Integrated differences (I) + moving-averaging (MA).
Ref.: https://people.duke.edu/~rnau/411arim.htm
Integrated (I): Using the differentiating a non-stationary time series to attempt to transform it to a stationary series.
Moving-Average (MR) model: Using the past forecasted error terms instead of the lags of the dependent variable.
Limitation of these AR methods
Ref. Modeling daily realized futures volatility with singular spectrum analysis Dimitrios D. Thomakosa, Tao Wanga, Luc T. Willeb; ∗ Received 29 November 2001 https://doi.org/10.1016/S0378-4371(02)00845-2Limited as the forecasting model: Whereas it provides the magnitude of the fluctuations e.g., volatility, their distribution is assumed to be symmetric. The error of the estimation expands for the future estimate
The coefficients of the lagged variable contain non-periodic noise terms, which disrupt the estimation of the important periodicities represented by these coefficients.
SSA and Prony-like methods can extract the cyclical physical components that contain important information about the periodicity and its magnitude by distinguishing them from the noise components.
SSA performs better than various auto-regressive methods and Hodrick-Prescott filter (Noise-filtering method) introduced in "Singular spectrum analysis for real-time financial cycles
measurement Maximilien Coussin 1 https://doi.org/10.1016/j.jimonfin.2021.102532"
Sunday, May 19, 2024



























