Hi,

I am working on a data set for my hierarchical time series forecasting project which has *3,064,489 X 7* data. Using this data set, I have created a *hierarchical tsibble object* as per the following code:

```
data |>
aggregate_key(Cluster/ County, Cases= sum(count))
```

The hierarchical time series has been created with below dimensions:

**A tsibble: 3,074,348 x 4 [1D]**

*# Key: Cluster, County [3,118]*

- Date Cluster County Cases*
- <int*> <int*> *

Using the model() function, I have created the below models (creating these models took almost 24 hours) with the following dimensions:

**A mable: 3,118 x 9**

*# Key: Cluster, County [3,118]*

- Cluster County Mean Naive SNaive Drift Ets Arima Neural*
- <int*> <int*> *

But when I am running the forecast function using all the above models simultaneously, even after 3 days its still running without any result.

I am not sure, if the time taken by forecast function is due to big chunk of data.

Please help me with mitigating the issue.

##
Thanks,

Sonaxy

^{Referred here by Forecasting: Principles and Practice, by Rob J Hyndman and George Athanasopoulos}