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PMI prediction explanation

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What is PMI prediction?

PMI uses a combination of machine learning (ML) and statistical models to automate forecasting of revenue and units (covers, room nights, guest nights etc.). It uses historical data to identify trends (seasons) and adapts to specific properties over time based on their activity.

How is PMI prediction calculated?

You can view the PMI prediction values in the Live forecast module. To learn more about a specific date’s prediction, click on the PMI prediction cell.

  1. In the top section you see the prediction details.
    1. Lead time: How far out is this day? E.g. tomorrows forecast has a lead time of 1.
    2. Seasons: The date comparison set used to forecast. See ‘Seasons’ section below.
    3. The text in brackets (S1, LeadT, DoW) are the forecasting models being used. See the ‘Forecasting models’ section below.
  2. The first table shows relevant historical dates.
  3. The final section is ‘discarded dates’ – dates that were in the correct season on the same week day but were disregarded in the forecasting algorithm as outliers. Hover over the red question mark to see the reason. See ‘Outliers’ section below.

Forecasting models

There are four different forecasting models (1 statistical, 3 Machine Learning), and each model generates a new forecast every day for the next 16 months. The model with the best accuracy for the forecasted weekday, at the current lead time, and for the specific property, will be used in PMI Prediction. This selection process is done separately for room nights and ARR.

1. Statistical model

  1. S1: Uses historical data from the season and weekday to forecast the future. It reacts instantly on any change that is done affecting the target date. As it is only using historical data, it can take time to react to new trends or situations.

2. Machine learning: The main benefit for ML models is that they learn each property separately and will adapt                the features according to the specific property. Over time, it will therefore improve the forecasting accuracy.

  1. LeadT (Lead time): Has one sub-model for each lead time. This means the features used and the                             importance of each differs depending on what lead time is being forecasted. Note – weekday is used as a             feature, so the day of the week still plays a role in this forecast.
  2. DoW (Day of the Week): Has one sub-model per weekday. Uses the day of the week to determine the                     importance of the features that create the forecast. Note – lead time is used as a feature, so the lead time           still plays a role in the forecast.
  3.  Comb (Combined): Uses an average of LeadT and DoW.
  4. Szn (Season): Has one sub-model per season. Places different weight to all used features (including weekday and lead time) depending on which season is being forecasted.

    3. Combination models: As the statistical and the ML models use different approaches to forecast, it is sometimes beneficial to combine these. If a combination is used, they are labeled with the model names but with slightly shorter abbreviations:

    1. LeadT becomes LT
    2. DoW becomes DW
    3. Szn becomes SZ

    So, if, for example, S1 and LeadT are used, it will be labelled S1/LT. Sometimes, the highest level of accuracy is achieved if an average of all the models is used. This combination would then be labelled ALL.


    What is a season?

    Seasons are groups of historical dates with similar behaviours in revenue and activity in the hotel. PMI Prediction uses seasons to identify dates that can be used to forecast the future. The more similar the dates are, the more likely it is that they “behave” in a comparable manner.

    Seasons are generated automatically based on the data in PMI. These cannot be edited.

    To view the seasons, go to the Live forecast > Tools > Seasons.

    The items with a grey frame around the date in the calendar are the rolling trend seasons (RTS). These are dates that create a bridge between seasons to give the algorithm more dates to work with, and make more recent history more important than previous years.

    What is an outlier?

    An outlier is something that is abnormal for the specific weekday and season. For example, If a property usually closes during Christmas, it is normal that these dates have 0 revenue. If one day then has sales, that day will become an outlier as 0 is considered as normal.

    A date set as an outlier will be disregarded when it comes to forecasting.

    Most outliers are detected automatically.

    Subject to user rights, you can also add manual outliers. To do this, go to Live forecast >Tools > PMI Live forecast > Tools > Seasons, and add a new label with an appropriate name. In the dropdown select ‘Outliers’.

    Outliers should only be used when there are significant changes in prices, offerings, or unusual events that are unlikely to happen again. Manual outliers should rarely be used for just a day or two, but instead when it is occurring on multiple days. 

    Remember that the auto detection of outliers will cover most of the dates that should be outliers, so the manual outliers is only a complement when the exception has occurred on so many days that the standard auto detection have considered them to be normal. 

    When is PMI Prediction updated?

    PMI Prediction is updated on scheduled intervals:

    1. Lead times 0-10 weeks: Daily, after a new OTB file and the results for yesterday are imported
    2. Lead times 11 weeks – 6 months: Once per week
    3. Lead times 6 months – 16 months: Bi-weekly

    When PMI Prediction is updated, the Live forecast for all days that are on automation will be refreshed.

    By hovering over the PMI Prediction value, a tool tip will reveal when it was last updated. This information is also displayed in the PMI Prediction popup.
    If there has been more, or less, booked since the last update of PMI Prediction, you can trigger an update manually by using the ‘Calculate Live forecast’ function.

    After accepting the new values, it might take a few minutes for all calculations to be completed, depending on the server activity. When the Live forecast is the same as PMI Prediction for days that are on automation, then the calculations are done, and you have a forecast based on the current OTB.

    Troubleshooting tips

    Navigating PMI Prediction Algorithm Accuracy

    Having trouble with the PMI prediction algorithm? Follow these streamlined steps to identify and fix data-related issues:

    • Verify Import Status: Begin by checking PMI's import status page to ensure data is being correctly updated. Any import problems will be flagged here.
    • Review Data Integrity: Use the drill-down feature to review imported data and verify its accuracy against your expectations.
    • Identify Outliers: Look for any anomalies in your data. Outliers can significantly impact automated forecasts, potentially skewing predictions.
    • Engage PMI Support: If after these checks the predictions still seem inaccurate, reach out to PMI support for deeper insights and potential adjustments for better accuracy.

    The PMI algorithm is designed to refine its forecasts continually. Variations are expected, particularly with new booking patterns or changes in cancellation rates. However, persistent discrepancies need attention to ensure reliable predictions.

    Ensuring Up-to-Date Data in PMI Predictions

    Understanding when and how PMI prediction data refreshes is key to maintaining accuracy and trust in the system. Here's a brief guide:

    • Data Refresh Cycle: PMI predictions are typically updated automatically at regular intervals. Daily refreshes handle short-term forecasts (0-10 weeks), weekly updates are for mid-range forecasts (11 weeks to 6 months), and biweekly updates manage long-term forecasts (6 months to 16 months).
    • Discrepancy Concerns: If you notice any discrepancies in the data, consider the last automatic refresh time. Data discrepancies right after an automatic update could point to potential import issues or the need for additional data review.
    • Manual Refreshes: In special cases where immediate data correction is needed, d2o support has the capability to trigger a manual refresh. This is particularly useful if significant data changes occur outside the regular update schedule, or if there are urgent corrections to be made.
    • Contacting Support: Should you need a manual data refresh or if you have concerns about the data integrity after the scheduled refreshes, reach out to d2o support. Provide them with the specifics of the issue, and they can guide you through the manual refresh process if necessary.
    • Monitoring Import data
    • PMI Prediction explanation