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PyTimeTK Roadmap #2
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We're Tracking Using GH Projects Now (Details Here)Project Plan - Timetk: https://github.com/orgs/business-science/projects/1 Please let me know if you would like to contribute and I will set you up as an Outside Collaborator. |
Hi mdancho84, Thanks for bringing timetk to python! Would you consider to build-in in phase 3, and X13ARIMA SEATS in phase 2? |
A nice extra addition to the augment module would be: In the sklearn example below, we see 12 spline features vs 2 Fourier, but a significant improvement in rmse and mae: |
We are evaluating modeling and forecasting next. Will keep you posted. It may be a separate package. |
Phase 1: MVP Package
Develop a minimal package with the most important functions.
Use this guide: https://py-pkgs.org/03-how-to-package-a-python
Priority 1 - Core Data and Data Frame Operations
summarise_by_time()
/summarize_by_time()
Priority 2 - Plot Time Series
plot_time_series()
- Not sure if we should go withplotly
oraltair
for interactive mode. I feel we should go withplotnine
for non-interactive. Will needsmooth_vec().
Priority 3 - Data Wrangling
future_frame()
- We will also needtk_make_future_timeseries()
andtk_make_timeseries()
pad_by_time()
Priority 4 - Augment Operations
Note - These functions should overwrite columns that are named the same in the input data frame.
tk.augment_timeseries_signature()
-tk.get_timeseries_signature()
tk.augment_holiday_signature()
- Usesholidays
packagetk.augment_lags()
/tk.agument_leads()
tk.augment_rolling()
tk.augment_fourier()
Priority 5 - TS Features
tk.ts_features()
Phase 2: Expand Functionality
Anomalize in Python
tk.anomalize()
Time Series Plotting Utilities
Time Series Inspection, Frequency, and Trend
tk.ts_summary()
Applied Tutorials
Phase 3: Extend Sklearn
Phase 4: Fill in Function Gaps Where Needed
Add additional functionality that was not identified in Phases 1-3.
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