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

• [x] summarise_by_time() / summarize_by_time()
• [x] Data Sets

Priority 2 - Plot Time Series

• [x] 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

• [x] future_frame() - We will also need tk_make_future_timeseries() and tk_make_timeseries()
• [x] pad_by_time()

Priority 4 - Augment Operations

Note - These functions should overwrite columns that are named the same in the input data frame.

• [x] tk.augment_timeseries_signature() - tk.get_timeseries_signature()
• [x] tk.augment_holiday_signature() - Uses holidays package
• [x] tk.augment_lags() / tk.agument_leads()
• [x] tk.augment_rolling()
• [ ] tk.augment_fourier()

Priority 5 - TS Features

• [x] tk.ts_features()

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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

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

Applied Tutorials

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

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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.