tensorflow / tensorflow/probability
STS Modeling Case Studies
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- Jupyter Notebook
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Description
Hi,
I am trying to make forecasts similar to examples in Structural_Time_Series_Modeling_Case_Studies_Atmospheric_CO2_and_Electricity_Demand.ipynb. One major differences is that I have multiple lists of data rather than one list. I am curious if it is possible for me to fit all data into the model. For example, say that I have CO2 data from Los Angeles, New York, San Francisco and Chicago, and I would like to predict amount CO2 next month in these four cities. One thing I could do is I use data from Los Angeles only to predict CO2 in Los Angeles next month. Is it possible to use all data from four cities to predict CO2 in Los Angeles?
Thanks
Contributor guide
First steps
- Read the whole issue, then the project's contributing guide.
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Research direction
Start with Structural_Time_Series_Modeling_Case_Studies_Atmospheric_CO2_and_Electricity_Demand.ipynb and trace how its single data list is passed into the structural time-series model. Determine whether the requested multi-city forecasting behavior is supported, and document the expected outcome or scope before attempting a change.
Written by the indexing model from the issue text.
Assessment
- Tech stack
- jupyter-notebook
- Domain
- data, machine-learning
- Issue type
- Feature
- Difficulty
- 4/5
- Estimated time
- 3-5 days
- Activity status
- Stale
- Clarity
- Needs clarification
- Newbie friendliness
- 25/100