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The seasonal ma polynomial (coeff =.88) is effectively cancelling the seasonal difference . I suggest that you simplify your model by eliminating the seasonal difference irrespective of the poor guida …

q=user%3A3382+daily+data for some very powerful examples and interesting discussions Simple ARIMA models get confused when weekends are different from weekdays and holidays/events have an effect what … The problem with simple ARIMA or SARIMA models for hourly/daily data is that the model structure is all endogenous (autoregressive). …

Since the PACF(2) is "more significant" than the ACF(2) this suggests an MA(1) model (0,0,1) . You might focus on the Q statistic for the suggested model as an attempt to test for sufficiency. This t …

When deciding between an AR model and an MA model one looks for dominance between the ACF and the PACF: If the ACF dominates then choose an AR model with the order dictated by the PACF. If the PAC …

of how temperature can be efficiently modelled using pseudo-causals ( seasonal dummies) identified from the data suggesting month of the year effects along with anomalies and a level shift rather than arima … Unwarranted arima differencing yields unecessarilily wide limits . …

Simply follow the paradigm presented here https://autobox.com/pdfs/ARIMA%20FLOW%20CHART.pdf and you will be good to go . …

The goal of ARIMA modeling is to separate the observed data to signal and noise .....this flowchart is useful to understand the why's and wherefores of ARIMA MODELLING. … Now the forecast equation is used to project forward based not only on any needed deterministic structure BUT the stochastic ARIMA structure. Hope this helps .... …

This would be called a Vector ARIMA problem and would be unwieldy as outlier /inliers cpuld distort parameter estimates. Standard errors would be microscopic in size due to large N . …

This is quite possible and is to be expected as all AR models can be expressed as MA models . If the ar(1) coefficient is .33333 as is your case it's negated inverse is -1/3 or -.33333. For example a …

Sometimes deterministic model are appropriate .. sometimes autprojective (ARIMA) are appropriate and more often both components are needed. In this case the deterministic component was a pulse . …

Your data suggest the need for an Intermittent Demand solution ... . I use AUTOBOX to identify a useful model identifying a three period interval between demands . It uses a sophisticated i.e. robust …

1,0,0)(0,0,0,)12 with coefficient .9999999 then you have (0,1,0)(0,0,0)12 the value of .999999 is used to illustrate a coefficient nearly 1 on another note if you need to incorporate seasonal dummies ARIMA …

By inspection the SARIMA model is (2,0,0)(1,0,0)12 because there is 1 ar polynomial with 2 coefficients with 0 differencing and 0 ma polyNomials THUS from left to right we have 2,0,0 Since there is …

day of the week it is what month your are in what level changes have occurred what trend changes have occurred what days of the month exhibit statistically usual effect what recent activity has been *arima …

After reviewing your 259,200 record detailing 60 readings per minute for 60 minutes for 72 days .. I suggest that you create two predictor variables for an ARMAX model. The first predictor will be hou …