45.0090.00

TUATI NOA

E rave rahi te mau mea e tano no te pee atu i te odds i te haamataraa 1.5 i nia i te 3.8

Maramarama

Faataaraa

Faahoro popo tuati noa Predictions

Faahoro popo tuati noa Predictions ,i numera, ang tohu ay isang bahagi ng inference. Te pupu atu nei matou ia outou papu tueraa popo predictions ma te odds i te 3.00 ma te faito teitei o te efficiency. Ang inference na ito ay predictive ng inference, Tera ra, e nehenehe te tohu e bolea i roto i te mau huru e rave rahi i roto i te numera inference. Mau, e nehenehe te hoe faaiteraa no te numera e horoa mai te reira i te hoe ravea no te transferring i te ite no nia i te hoe o te mau taata i te taatoaraa o te huiraatira, e te tahi atu mau populations, e ere te reira i te mea au roa i te tohu i te taime hoe. To tatou te mau ravea no te tia i te bet ang matai ilawaki na ito ay ang mga tipsters sa matete. Ia ilipat te haamaramaramaraa i te mau taime atoa, pinepine i te mau tuhaa taa e i te taime, Ua parauhia te faanahoraa ei forecasting.E titau te Forecasting i te taime no te mau ravea no te mau hora, e mea pinepine te tohu i te ravehia i nia i te sectional itepiri.

Te mau ravea faataaraa tei faaohipahia no te tohu, ua riro ia ei regression tuatapaparaa e tona mau Afirita e rave rahi tuhaa mai te hoe regression, generalized hoe huwaran (logistic regression, Poisson regression, Probity regression), tahi atu a. Te huru o te forecasting, Ua riro te regressive i te huwaran e te vector Auto regression huwaran e nehenehe e faaohipahia. I te taime e aore ra, te, generalized te regression e aore ra te matini o te afata apo deployed i roto i te tapihooraa usage, Ua parauhia te aua ei predictive analytics.

I roto e rave rahi mau faaohiparaa, mai te taime tuatapaparaa, e nehenehe te reira e faito i te huwaran e faatupu i te mau manao. Mai te mea e, e huwaran mai te mau ohipa transfer e aore ra, i roto i te hoe vahi parameters i muri iho e smoothed, e nehenehe ta filtered e tohu i te rahiraa itepiri e opuahia.Mai te mea e, e hoe pue te mau huwaran i raro ae i te faito, e riro ia te reira ei hoe variance Kalman e e faaohipahia te reira i roto i te huru o te anaanatae o faito variance. Na teie mau ravea e turui i nia i te hoe taahiraa i mua i te predictors (na vai e minimise i te variance o te tohu hape). Nang pue ang huwaran ay nonlinear na stepwise linearizations ay maaaring sa tila Kalman at smoother recursions. Tera ra, i roto i nonlinear taime, optimum faito-variance te ohipa te ore e faaohipa faahou.

A pee i te, e mai ia tatou:

Te tahi atu mau haamaramaramaraa

ODDS

1.5+, 1.9+, 2.2+, 3+