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my time series data looks like that:enter image description here

I would like to use ARIMA model to forecast next steps in time series. Unfortunately because of increasing variance data is non-stationary. Is there any way to transform my data to get rid of this variance? In addition before training I normalised data. Is normalisation required by ARIMA model?

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    $\begingroup$ Try a variance stabilizing transform, like the log. $\endgroup$ – Demetri Pananos Jan 31 at 5:51
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    $\begingroup$ What demetri said is fine. You could also try to model the process directly by saying possibly $y_t = \mu + \epsilon_t$ where the variance of $\epsilon$ is say (arch or garch ) or stochastic ( so SV model ). Just another possibility. $\endgroup$ – mlofton Jan 31 at 6:20
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As Demetrios says, you could try transforming your data to stabilize the variance. The classical approach is to use a Box-Cox transformation. One special case of this is the log transform. Some software will estimate a Box-Cox transformation parameter $\lambda$ for you.

Alternatively, as mlofton says, you could consider modeling your variance directly using an ARCH or a GARCH model. Your data does look somewhat like a financial returns series to me, with an essentially constant mean. In finance, we are often most interested in modeling variances, not so much means, and ARCH/GARCH is custom-made for this.

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  • $\begingroup$ in fact this is financial data. Thanks for great tip! $\endgroup$ – Sayaki Jan 31 at 21:09

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