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I have a time series with the following ACF and Partial ACF plots; however, I am a little confused on how to interpret these. … My thoughts: The ACF plot shows that the model does a satisfactory job based on the autocorrelation. …

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. …

But, ACF and PACF results are seems to different than other plot that I have seen. … like below. from statsmodels.graphics.tsaplots import plot_pacf plot_pacf(ts, lags = 40, method = "ols"); from statsmodels.graphics.tsaplots import plot_acf plot_acf(ts, lags = 50); What I need to interpret …

Following are acf and pacf plots of a monthly data series. The second plot is acf with ci.type='ma': The persistence of high values in acf plot probably represent a long term positive trend. … 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 …

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.) …

Can you please tell me what should be the values of ACF and PACF from the graphs I have attached? I think it should be ($p=0$, $d=1$, $q=3$). … I have differenced the data once so $d=1$ and there are three spikes in ACF so $q=3$. But function auto.arima from "forecast" package in R is giving the answer as (0,0,1). …

In order to determine (p, d, q) and (P, D, Q, m), I plotted the ACF and PACF. The ACF graph shows that there is a seasonality of 24 intervals, so I set the m as 24. … In other example, ACF graph usually degrades exponentialy, so they set p and q as 1 or 2. But my ACF graph has cycles, so I am confused about how to set p and q. Could you help me? …

Can somebody please help me understand how to interpret these plots and what conclusion should I arrive to about the p, d, q parameters of an ARIMA model for this data? Thanks in advance. … EDIT: Below are the ACF/PACF plots for the original time-series: …

These are the plots I got after performing ACF and PACF, the plot on the bottom is the log-transformed volume shifted by one week. However, I am confused by how should I interpret the results. …

I have these data, related to value of pH over time: I want to generate a stochastic model (ARMA, ARMAX, and so on), and I want to decide the order of this model, so I did the plots of ACF and PACF, because … But I cannot interpret these plots, because I think some pre-processing steps are required. …

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. …

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? …

Used the residuals and plotted ACF and PACF but have no idea on how to interpret this. I am noticing seasonality (all I understood). …

I am not very good at Time Series, so I have a few questions about my ACF & PACF plots. I have read through similar questions on stack exchange and other websites, but their graphs look different. … I understand somewhat how to interpret the plots, but mine do not fit the stereotypical "molds". They are not slowly decreasing, they don't seem to be significant until a certain p. …

tsdata_decompose)) tsdata_recompose <- tsdata_decompose$trend + tsdata_decompose$seasonal + tsdata_decompose$random tsdata_error <- tsdata_recompose - tsdata Using the checkresiduals() functions i get How can I interpret … the ACF plot? …