Farzad E
01/19/2023, 6:06 PMFarzad E
01/19/2023, 6:12 PMsf = StatsForecast(
df=df,
models=models,
freq='W',
n_jobs=-1,
ray_address='10.10.10.110:6379'
)
forecasts_df = sf.forecast(h=52, level=[90])
I don't have a yaml file though. I start my ray cluster on my EC2 instance and then pass the address to StatsForecast.fede (nixtla) (they/them)
01/19/2023, 6:38 PMn_jobs=-1
might be best. StatsForecast uses a map reduce approach. So if you have 10 time series and 32 cores available, statsforecast will use 10 cores to train (one for each series). The training speed of those time series will depend on the models used and the length of the series. For example, models like autoarima in very long time series (more than 100 observations) are usually very slow. Models like MSTL tend to be faster.Farzad E
01/19/2023, 7:06 PMfede (nixtla) (they/them)
01/19/2023, 7:28 PMFarzad E
01/19/2023, 7:30 PM