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| Marketplace | CM Forecast Data

CM Forecast Data

1.0.0 0
This machine forecasts Rooms Sold by closely tracking how bookings build up over time. It uses pickup logic to understand day-by-day changes in reservations, learning from historical booking behavior and current on-the-books data. By observing how demand naturally evolves as arrival dates approach, the machine produces a dependable Rooms Sold Forecast that reflects real booking patterns. This forecast supports informed planning and helps teams anticipate future demand with greater confidence.

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Machine Overview
  • Machine Name : CM Forecast Data
  • Title : CM Forecast Data
  • Short Description :
    This machine forecasts Rooms Sold by closely tracking how bookings build up over time. It uses pickup logic to understand day-by-day changes in reservations, learning from historical booking behavior and current on-the-books data. By observing how demand naturally evolves as arrival dates approach, the machine produces a dependable Rooms Sold Forecast that reflects real booking patterns. This forecast supports informed planning and helps teams anticipate future demand with greater confidence.
  • Detail Description :

    The process begins by securely receiving database credentials from the upstream machine, allowing controlled access to hotel booking and occupancy data. Once connected, the machine extracts historical, on-the-books, and pickup-related records required for forecasting.


    Using this data, the machine applies pickup logic to calculate key indicators such as DBA (Day Before Arrival) and PU (Pickup), helping reveal booking pace, demand build-up, and shifts in booking behavior as arrival dates approach.


    Based on these insights, the machine generates an Occupancy Forecast. In parallel, it incorporates Seasons defined by Revenue Managers, which provide suggested room rates aligned to demand periods and pricing strategies.


    The suggested rates from the seasonal setup are combined with the forecasted occupancy to calculate Forecasted Revenue. By multiplying the expected rooms sold with the recommended rates, the machine produces a revenue outlook that reflects both demand trends and pricing intent.


    Throughout the workflow, the machine performs data validation and consistency checks, handles missing values and irregular booking patterns, and maintains detailed logs for transparency and traceability.


    The final, structured output feeds directly into business intelligence dashboards and downstream revenue and pricing systems, supporting confident, data-driven decision-making.

  • Machine Document : -
Classification
  • Industry : Hotels
  • Category : Forecast
  • Sub Category : -
Compatibility and Dependencies
  • Dependent Machines : -
  • Compatible Machines : -
  • Version : 1.0.0
Specifications
  • Input Specification : Download Gets database credentials to connect to master Database and endays to genrate demand score client_id: Integer property_code: String db_username:String db_password:String db_host:String db_port:Integer db_name:String forecast_days:Integer
  • Output Specification : Download

    Returns the following outputs for downstream processing:
     

    • Client ID (Integer): Unique identifier for the client.
    • Property Code (String): Unique code representing the property.
    • Database Username (String): Username for the Admin database.
    • Database Password (String): Password for the Admin database.
    • Database Host (String): Host address of the Admin database.
    • Database Port (Integer): Port of the Admin database.
    • Database Name (String): Name of the Admin database.
Infrastructure Requirement
  • OS Requirement : Linux
  • CPU : 8
  • Cloud : AWS
  • RAM : 16Gi
Usage Stats
  • Total OnBoarded : 8
  • Active OnBoarding : 8
Parameter Name Type Description
Client Id Number
Property Code Text Box
Db Username Text Box
Db Password Password
Db Host Text Box
Db Port Number
Db Name Text Box
Forecast Days Number

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January 12, 2026

Updated the sql query to solve the season data overlap issue.


January 9, 2026

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Total Ratings
0
Average Rating
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Star Rating
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This machine supports pickup-based forecasting by analyzing booking build-up over time to generate a Rooms Sold forecast. It also combines this forecast with season-based suggested rates defined by Revenue Managers to produce forecasted revenue for planning and decision-making.

DBA (Day Before Arrival) and PU (Pickup) are key indicators used to track how bookings change as the arrival date approaches. They help measure booking pace, demand buildup, and shifts in reservation behavior over time.

Seasons created by Revenue Managers define suggested room rates for different demand periods. These rates are applied to the forecasted Rooms Sold to calculate forecasted revenue, ensuring the forecast aligns with both demand trends and pricing strategy.

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noDataFound

Data Not Found

Specifications
  • Input Specification : Download Gets database credentials to connect to master Database and endays to genrate demand score client_id: Integer property_code: String db_username:String db_password:String db_host:String db_port:Integer db_name:String forecast_days:Integer
  • Output Specification : Download

    Returns the following outputs for downstream processing:
     

    • Client ID (Integer): Unique identifier for the client.
    • Property Code (String): Unique code representing the property.
    • Database Username (String): Username for the Admin database.
    • Database Password (String): Password for the Admin database.
    • Database Host (String): Host address of the Admin database.
    • Database Port (Integer): Port of the Admin database.
    • Database Name (String): Name of the Admin database.

Updates and Community

Machine Developer

BotCraft Engineers
  • Last Updated
  • January 12, 2026
  • Version
  • 1.0.0
Version History

Statistics

Total Workflows
8
Active Workflows
8
Success Rate
100
Last Used in Workflow
N/A

Categories

Forecast

Support

Average Response Time 8AM-8PM