I have a bunch of data frames from different data sources. They are all time series data in order of a column timestamp, which is an int32 Unix timestamp. I can join them together by this and another column join_idx which is basically an integer inde...
@Erik Louie​ :If the data frames have different time zones, you can use Databricks' timezone conversion function to convert them to a common time zone. You can use the from_utc_timestamp or to_utc_timestampfunction to convert the timestamp column to ...
I'm training a NeuralProphet for a time series forecasting problem. I'm trying to parallelize my training, but this error is appearingThe folder lightning_logs has a hparams.yaml but it's empty. Is this related to permissions on the cluster? Thanks i...
We have some timeseries in databricks, and we are reading them into powerbi through sql compute endpoints. For timeseries powerbi is ... not optimal. Earlier I have used grafana with various backends, and quite like it, but I cant find any way to con...
Hello,I am currently working on a time series forecasting with FBProphet. Since I have data with many time series groups (~3000) I use a @pandas_udf to parallelize the training. @pandas_udf(schema, PandasUDFType.GROUPED_MAP)
def forecast_netprofit(pr...
Thank you for the answers. Unfortunately this did not solve the performance issue.What I did now is I saved the results into a table:results.write.mode("overwrite").saveAsTable("db.results") This is probably not the best solution but after I do that ...
