PyTimeTK Roadmap

[mdancho84]

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()
• Data Sets

Priority 2 - Plot Time Series

• plot_time_series() - Not sure if we should go with plotly or altair for interactive mode. I feel we should go with plotnine for non-interactive. Will need smooth_vec().

Priority 3 - Data Wrangling

• future_frame() - We will also need tk_make_future_timeseries() and tk_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() - Uses holidays package
• tk.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

• Convert Anomalize R package to tk.anomalize()

Time Series Plotting Utilities

• Plot ACF
• Plot Anomalies
• Plot Seasonality
• Plot STL Decomposition
• Plot Time Series Regression

Time Series Inspection, Frequency, and Trend

• TS Summary: tk.ts_summary()
• Time Scale Template
• Automatic Frequency Detection
• Automatic Trend Detection

Applied Tutorials

• Sales CRM Database Analysis
• Finance Investment Analysis
• Demand Forecasting
• Anomaly Detection
• Clustering

---

Phase 3: Extend Sklearn

• Time Series Splitting / Cross Validation Functionality
• Preprocessors & Feature Engineering
• Vectorized Functions - Box Cox,
• Plot Time Series CV

Phase 4: Fill in Function Gaps Where Needed

Add additional functionality that was not identified in Phases 1-3.

[mdancho84]

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.

[isachng93]

Hi mdancho84,

Thanks for bringing timetk to python!

Would you consider to build-in in phase 3, and X13ARIMA SEATS in phase 2?

[joshdunnlime]

A nice extra addition to the augment module would be: tk.augment_periodic_spline. This has can have accuracy benefits over Fourier encoding, but does come at the cost of many more features.

In the sklearn example below, we see 12 spline features vs 2 Fourier, but a significant improvement in rmse and mae:
https://scikit-learn.org/stable/auto_examples/applications/plot_cyclical_feature_engineering.html#periodic-spline-features

[mdancho84]

@isachng93

Would you consider to build-in in phase 3, and X13ARIMA SEATS in phase 2?

We are evaluating modeling and forecasting next. Will keep you posted. It may be a separate package.