Discussions

I'm experiencing timeouts when setting fine_tune_steps >= 370 during model training with the following configuration: - **Dataset Size**: 17k rows - **Model**: timegpt-1-long-horizon - **Key Parameters**: - fine_tune_steps=400 - clean_ex_first=False - id_col="id" - add_history=False - df=df_exog1 - h=48 - target_col='--------' - time_col="DateTime_x" - freq="min" Despite having sufficient resources in Google Colab, the process times out consistently when fine_tune_steps is set to 370 or higher. **What could be causing these timeouts, and is there a recommended solution? Are there any subscription plans or higher-performance APIs available that can handle more extensive fine-tuning without these issues?**

I am trying to fetch an API key but receiving a white blank page in response while clicking on 'Get Api Key' tab. Can anyone please help me with this?

while plotting anomaly, how can we know the exact date of the plotted anomaly.

It seems like it is nearly shut down.

It seems like it is nearly shut down.

Which API key? What? Nobody knows? Where are the time columns? No time columns? Is it invariant to column name permutations? Is it invariant to column permutations? How to identify the target predictor? What is the task form? P(Y'|X, y, X') is not supported?

Following the quick start guide, these plots do not appear when calling nixtla_client.plot() Could you please add more information on IDEs preferred, environments, and additional dependencies? Running in a conda environment there are dependency problems with numpy and matplotlib. Notebooks will not work, from this approach, and the VS Code IDE will not display plots.

how to feed endogenous variables to help in forecasting ?, forecast function call can only see options for feeding exogenous variables.

does nixtla predict well for day-wise prediction, because im getting a huge difference in the actual and predicted values .

I would like to know how can we use your services in cases in which data are confidential and are not allowed to leave our servers? Is there a way to run the model locally? How does your Azure implementation work in that respect? Thank you.