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ACF and PACF plot analysis Help interpreting ACF- and PACF-plots Help understanding the following picture of ACF Autocorrelation and partial autocorrelation interpretation Edit: following is the graph … Only acf/pacf plots of residuals was referred to in a couple of places. …

I want to see if I am on the right track analysing my ACF and PACF plots: Background: (Reff: Philip Hans Franses, 1998) As both ACF and PACF show significant values, I assume that an ARMA-model will … sufficiently, b.) whether the ACF signals overdifferencing and c.) whether the ACF and PACF show any significant and easily interpretable peaks at certain lags ACF and PACF might suggest not only one …

So I plotted the ACF/PACF of oil returns and was expecting to see some positive autocorrelation but to my surprise I only get negative significant autocorrelation. … How should I interpret the above graph? They seem to indicate that there is a tendency for oil returns to increase when it decreased previously and vice-versa, thus the oscillating behaviour. …

I am presenting some ACF and PACF plots to colleagues. … I can explain how to interpret the plots and how to determine p and q based on what the plots look like, but I cannot find a simple intuitive explanation of what a PACF plot actually means. …

10253280}, {{2012, 11, 12}, 10240672}, {{2012, 11, 06}, 9723456}, {{2012, 11, 13}, 9806880}, {{2012, 10, 16}, 12368896}, {{2012, 11, 18}, 9632800}, {{2012, 10, 27}, 10606656}}] ... and the ACF … : ... and the PACF: …

I'm having trouble interpreting an ACF/PACF plot of the residuals of a regression to determine what the corresponding ARIMA model would be for the error term. … This is the plot of the ACF/PACF of the regression. …

I just want to check that I am interpreting the ACF and PACF plots correctly: The data corresponds to the errors generated between the actual data points and the estimates generated using an AR(1) … For the ACF this is the case but for the PACF there are about 10 exceptions. If you include those on the border it's more like 14? Does this still indicate no auto-correlation? 3.) …

The ACF shows a positive impact of the first lagged term while the PACF shows a negative impact? Could someone help me interpret this? I'm trying to better understand ARIMA. … The examples I've seen about PACF and ACF always seem to show the two at least agreeing in direction. …

It is obvious that there is still a seasonal component to the data from the ACF plot. I then tried to use differencing to remove the seasonal component. … How do I interpret the seasonality of the Log differenced plot and model the data ? …

I am learning about ACF and PACF graphs. I am not sure I understand how to interpret the one I got for my data. … I have searched google for some ACF and PACF examples, and I found some samples of different processes, however, the one I am getting doesn't look similar to any. …

usp=sharing The plot below shows the series: The ACF and PACF plots are shown below: The ACF clearly shows yearly seasonality (12 periods). … I have tried to use first-differencing and second-differencing (in case either one or both are needed), below is the respective ACF and PACF plot: My question is: How to interpret the PACF plot of …

My ACF- and PACF plots are illustrated below: The first one is in original scale and the second picture is zoomed. What process would you classify this? AR, MA or ARMA? …

New analysis I have now plotted the PACF and it looks as follows: As you can see, it looks fairly similar to the ACF. … (as.numeric(BTC$log_returns), main="ACF of Bitcoin log return series") # Higher order autocorrelation I would say Pacf(as.numeric(BTC$log_returns), main="Partial ACF of Bitcoin log return series") #The …

The diagnostics of the residuals are all good (ACF, PACF, ...), i.e. it seems they behave like white noise. But when I plot the normal qq-plot, they seem to be asymmetrically distributed. … My only problem is that now, I do not know how to interpret this result. What does it mean for the residuals to follow a Student's $t$ distribution? …

0.1150526 My question is this: based on the parameter estimates and s.e. of the regressors, I believe that none of them are significant - is this correct, and if so, what does it imply if my goal is to interpret … Here are the ACF and PACF for the residuals: > durbinWatsonTest(mod.ols, max.lag=12) lag Autocorrelation D-W Statistic p-value 1 0.120522674 1.6705144 0.106 2 0.212723044 1.4816530 …