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There are 96 observations of energy consumption per day from 01/05/2016 - 31/05/2017. I am trying an ARIMA model in R to be fitted to these time series observations. I have chosen the frequency of time series as 96. In total, there are 38016 observations.
I have cleaned the series using `tsclean`

command in R to remove the outliers.

```
timeseries <- ts(full$consumption, frequency = 96)
```

Cleansed time series:

```
timeseries <- tsclean(timeseries)
```

I have then differenced the series:

```
diffts <- diff(timeseries)
```

I have then decomposed the series to obtain the seasonal component from it. Subtracted the seasonal component from it to deseasonalize it.

```
difftscomponent <- decompose(diffts)
adjusted_diffts <- diffts - difftscomponent$seasonal
```

The ACF plot of final time series:

```
acf(adjusted_diffts)
```

The PACF of the final time series:

```
pacf(adjusted_diffts)
```

There are three questions:

Normally, the X-axis of ACF and the PACF plot of the time series will show lag order from 1 to ... . There will be integer values indicating the number of lags. Then why in my case there are decimal values from 0.1.... ? What does that indicate? What to do to get number of lags?

What will be the probable order of AR and MA in my case by looking at ACF and PACF plot?

I have transformed the original time series i.e. made it stationary and deseasonalized it in order to fit an ARIMA model. By looking at the final adjusted time series, can it be said that it is fit to be modelled OR will it require any further transformation?

I'm voting to close this question as off-topic because its a basic time-series question and you'll find more time-series analysts on http://stats.stackexchange.com

– Spacedman – 2017-07-12T15:59:22.407