Run TimeGPT in a distributed manner using Dask for scalable forecasting.
Step 1: Installation
Install Fugue and Dask
fugue
with:nixtla
library is installed on all worker nodes.Step 2: Load Your Data
unique_id | ds | y | |
---|---|---|---|
0 | BE | 2016-10-22 00:00:00 | 70.00 |
1 | BE | 2016-10-22 01:00:00 | 37.10 |
2 | BE | 2016-10-22 02:00:00 | 37.10 |
3 | BE | 2016-10-22 03:00:00 | 44.75 |
4 | BE | 2016-10-22 04:00:00 | 37.10 |
Step 3: Import Dask
Step 4: Use TimeGPT on Dask
NixtlaClient
class to interact with Nixtla’s API.Using an Azure AI endpoint
base_url
parameter:NixtlaClient
, such as forecast
or cross_validation
.unique_id | ds | TimeGPT | |
---|---|---|---|
0 | BE | 2016-12-31 00:00:00 | 45.190453 |
1 | BE | 2016-12-31 01:00:00 | 43.244446 |
2 | BE | 2016-12-31 02:00:00 | 41.958389 |
3 | BE | 2016-12-31 03:00:00 | 39.796486 |
4 | BE | 2016-12-31 04:00:00 | 39.204533 |
Azure AI Models
model
to "azureai"
:timegpt-1
(default)
• timegpt-1-long-horizon
See the Long Horizon Forecasting Tutorial for details on timegpt-1-long-horizon
.