Release Notes

Each version of Monument adds capabilities and robustness to the platform.

Upgrade at no cost to the latest version by following the link at Help > Check for Updates...

Q1 2021 - User-defined Columns and Stacked Models

(release date 2021-03-05)
  • User-defined columns with user-defined functions
    • Over 40 functions to modify and enhance your data, listed here at User-defined Columns
    • Modify your training data within Monument
    • Create additional data features to enhance model performance
  • Model stacking
    • Capabilities to stack, combine, ensemble, and weight models
    • Estimate cluster groupings
    • Create estimates of error rates
  • Code signing
    • Increased security across Windows and macOS platforms via code-signing

Q4 2020 - Model Serving

(release date 2020-12-30)
  • Model serving
    • Run your trained model on new, out-of-sample data

Q4 2020 - Autopilot and Model Comparison

(release date 2020-12-08)
  • Autopilot: automatic algo selection
    • Select your target variable and let Monument analyze which analytic methods are most suitable
  • Model benchmarking and comparison
    • Is this model useful? Determine the performance of your model relative to a naive benchmark
    • Is this model better than the prior edition
  • Multiple plots and nested plots
    • Multiple dimensions now show multiple plots or nested data, as appropriate in the UI

Q4 2020 - LSTM Variants

(release date 2020-11-16)
  • Multi-layer Deep LSTMs
    • LSTMs with multiple hidden LSTM layers where each layer contains multiple memory cells, each fed into the subsequent layer
  • Phased LSTM
    • LSTM variation emulating the spiking nature of neurons belonging to 3rd Gen of neural nets
  • **Rescaled LSTM*
    • LSTM variation that enhances LSTM memory performance with Gaussian-Process downsampling

Q3 2020 - Cross-sectional Data Handling

(release date 2020-08-25)
  • Cross-sectional and panel data algorithms for both numerical and categorical data
  • Adaptive algo display with predictive methods that automatically adapt to the currently selected data (for example, displaying a date enables timeseries methods)
    • Algos are shown only where appropriate for the selected data.
    • When a Dimension is moved into the charting area, only classification algos are shown and run.
    • When a time pill is moved into COLS, only timeseries algos are shown.
  • New cross-sectional data methods
    • LGBM for cross-sectional and panel data, with AutoML
    • SVM for categorical data and SVR for numerical cross-sectional and panel data
  • Feature importance for LGBM, G-Boost, and Ent-Boost
  • Sample datasets available at Help > Demos
  • Further training and guidance available at Help > FAQ and YouTube links
  • Error edge-case cleanup with 95% reduction in error messages, supported by enhanced, user-driven error reporting

Q3 2020 - Windowing

(release date 2020-07-20)

  • Partitioning methods in windowing enable out-of-sample cross validation across an entire time series
  • Shuffle windowing methodology cleanup with shuffle discontinued in dlm, lightbml2, tvar, gsboost, entboost. Shuffle in these instances enabled look-forward bias.
  • Binance-third-party data integration
    • Binance rewritten to allow any valid starting date (previously limited to a single call, typically 500 entries). Also checks for existing values to minimize re-reads.
  • Bug fixes