The forecast_moment is the point in time (in UTC) the forecast was created.
forecast_distances are the number of hours forecasted ahead, relative to the forecast_moment.
For instance, imagine a forecast created at 8am with a forecast_distance of 3 hours suggesting rain. This means the model expects the rain to happen around 11am.
Weather forecasting models are quite resource intensive and take time to run before results become available.
We make sure to extract and process the data in the quickest way, however some delay does still occur.
When training a machine learning model it is advised to take the live delays into account, for instance by mapping the historical data to a delayed timestamp as well.
Future releases are already scheduled to address this.
HTTPError: HTTP Error 429: Too Many Requests.This error usually appears when you've called the API via pd.read_json(url) and you've exceeded the data limit. Your data limit can be increased simply by signing up for free. If you're also exceeding your free plan data limit, consider upgrading your plan.
ValueError: If using all scalar values, you must pass an index.This error usually appears when you've called the API via pd.read_json(url) and something went wrong. Please check out the API reference and try calling the API using the Python requests package or using our blueskyapi Python client. Often this error comes from using a wrong API key, please have a look if the key you may have used matches one of your API keys.
HTTPError: HTTP Error 422: Unprocessable Entity.This error usually appears when you've called the API via pd.read_json(url) and something went wrong. Please check out the API reference and try calling the API using the Python requests package or using our blueskyapi Python client. Often this error comes from using parameter values that are faulty or not allowed in the free plan. In the latter case, consider upgrading your plan.