Approach
While Tableau was used for visual representation, the CRG team harnessed Python libraries using deep learning techniques to generate accurate forecast. The following components were used to achieve end-to-end automation:
- Tableau for extracting historical data.
- TabPy to connect & run the Python program
- Leveraging Python libraries like FB Prophet, LSTM, Neural Prophet, Sarimax & Garch etc.
- TABCMD Command line extension to read forecasted values and save in a file
- Tableau Prep to read the file and populate structured Snowflakes data base table.
Problem Statement
Company wants to use Python libraries to create forecasting Models to improve their learning and at the same time they want to use Tableau as visualization tool with following automated process:
a) Execute the models periodically
b) Store forecasted values of each run date for future reference
c) Generate forecasting efficacy report with comparison of actual vs. forecast
High level Solution Architecture:
Briefly:
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