# Get ML Features Access prebuilt event-based Machine Learning features that will take your forecast models and results to the next level, fast. We've built up years of expertise in transforming raw event data into meaningful demand signals. Across industries, we’ve consistently seen that naïve aggregation produces noise rather than uplift. The Features API encapsulates that experience - delivering proven, engineered signals that improve forecast accuracy without the heavy lifting. ## POST /v1/features/ > Get ML Features ````json {"openapi":"3.1.0","info":{"title":"PredictHQ Features API","version":"1.0.0"},"tags":[{"name":"Features API"}],"servers":[{"url":"https://api.predicthq.com"}],"security":[{"BearerAuth":[]}],"components":{"securitySchemes":{"BearerAuth":{"type":"http","scheme":"bearer","bearerFormat":"$API_KEY","description":"Enter your PredictHQ API key. The Bearer prefix is added automatically.\n\nWhen calling the API directly, send: `Authorization: Bearer ` as documented at [https://docs.predicthq.com/api/overview/authenticating](https://docs.predicthq.com/api/overview/authenticating)\n"}},"parameters":{"OffsetParam":{"name":"offset","in":"query","description":"Offset the results.","required":false,"schema":{"type":"integer","default":0}},"LimitParam":{"name":"limit","in":"query","description":"Limit the number of results.","required":false,"schema":{"type":"integer","default":10}}},"schemas":{"FeaturesRequestModel":{"type":"object","properties":{"active":{"description":"The date range to calculate features for. This is named `active` because it includes events that are active within the date range. A multi-day event might start or end outside the specified date range - the days the event is active within the specified range will be included in the calculations.\n\n**Supports the following fields:**\n- `gt` - greater than\n- `gte` - greater than or equal\n- `lt` - less than\n- `lte` - less than or equal\n\nNote that all dates here are in local time (not UTC). Features API works on specific locations.\n\n\n**E.g.**\n```json\n{\n \"active\": {\n \"gte\": \"2019-11-28\",\n \"lte\": \"2019-11-29\"\n }\n}\n","$ref":"#/components/schemas/ActiveDateRange"},"beam":{"description":"An optional beam analysis object which if provided will calculate the `location`, `interval`, `week_start_day`, `phq_*` features and `stats`, and `phq_rank` from the Beam analysis (and optionally its group).\n\n**Fields:**\n- `analysis_id` (required if `beam` is present): Beam analysis ID\n- `group_id` (optional): Beam analysis group ID\n\nIf beam.analysis_id is provided, the `location`, `interval`, `week_start_day` , `phq_*` features including `stats` and `phq_rank` will be calculated from the Beam analysis.\n\n**E.g.**\n```json\n{\n \"beam\": {\n \"analysis_id\": \"0SXAmHsoZo0\"\n }\n}\n```\nIf both `beam.analysis_id` and `beam.group_id` are provided, all the above fields will be calculated from the Beam analysis but the `phq_*` features will be calculated from the Beam analysis Group.\n\nNote: This is useful for when you want to request features for a variety of locations while maintaining feature consistency by limiting the returned features to those of the group's feature importance results.\n\nE.g.\n```json\n {\n \"beam\": {\n \"analysis_id\": \"0SXaMHsoYo0\",\n \"group_id\": \"QxE9BcLq7ZY\"\n }\n}\n```","$ref":"#/components/schemas/Beam"},"hour_of_day_start":{"description":"Time range (per day) to calculate features for.\n\nNote: This field is currently only supported on `phq_viewership_*` features.\n\nIf your location only operates within certain hours of the day you can use this filter to only include records that are happening within those hours.\n\nSupports the following fields:\n**Fields:**\n- `gt` - greater than\n- `gte` - greater than or equal\n- `lt` - less than\n- `lte` - less than or equal\n\nThe values are hours between 0 and 23 (i.e. 24h format).\n\nE.g. only include events happening between 1pm and 3pm each day.\n\n**E.g.**\n```json\n{\n \"hour_of_day_start\": {\n \"gte\": 13,\n \"lte\": 15\n }\n}\n```\n","$ref":"#/components/schemas/HourOfDayRange"},"location":{"description":"Location to calculate features for. You can specify the location as a latitude/longitude (with radius), Place ID(s) or [Saved Location IDs](https://docs.predicthq.com/api/saved-locations/overview). \n\nWe recommend using a lat/lon+radius or a saved location id (for a point and radius location) as they could define the location of your interest more accurately. To work out a suitable radius around your location we strongly recommend using our [Suggested Radius API](https://docs.predicthq.com/api/suggested-radius/get-suggested-radius).\n\nNote: When using Place IDs or Saved Location IDs a maximum of 3 IDs may be used.\n\nNote: When using lat/lon+radius, the radius must be in the format ``, where `` is an integer or a float number up to 2 decimal places and `` is one of:\n- `m` - meters\n- `km` - kilometers\n- `ft` - feet\n- `mi` - miles\nE.g. using Place IDs:\n```json\n {\n \"location\": {\n \"place_id\": [\n 5224323,\n 5811704,\n 4887398\n ]\n }\n }\n```\nE.g. using Saved Location IDs:\n```json\n {\n \"location\": {\n \"saved_location_id\": [\n \"BN7ZSw8xza9FviPVfyCycd\",\n \"X3uyTFbDOUaX2q_Qh5i31b\",\n \"X3uyTFbDOUhrfq_Qh5i31A\"\n ]\n }\n }\n```\nE.g. using a latitude/longitude and radius:\n```json\n {\n \"location\": {\n \"geo\": {\n \"lat\": 41.75038,\n \"lon\": -71.49978,\n \"radius\": \"2.62mi\"\n }\n }\n }\n```\n","$ref":"#/components/schemas/Location"},"interval":{"description":"Aggregation interval.\n\nPossible values:\n **Fields:**\n- `day` (default) for daily aggregation\n- `week` for weekly aggregation\n","$ref":"#/components/schemas/Interval"},"week_start_day":{"description":"The weekday to be treated as the start of the week.\n\nPossible values:\n- `monday` (default)\n- `tuesday`\n- `wednesday`\n- `thursday`\n- `friday`\n- `saturday`\n- `sunday`\n\nOnly applicable when interval is set to week.\n","$ref":"#/components/schemas/Weekday"},"predicted_events":{"description":"Include or Exclude predicted events.","$ref":"#/components/schemas/PredictedEvents"},"phq_attendance_academic_graduation":{"description":"Academic - Graduation","$ref":"#/components/schemas/StatsField"},"phq_attendance_academic_social":{"description":"Academic - Social","$ref":"#/components/schemas/StatsField"},"phq_attendance_community":{"description":"Community","$ref":"#/components/schemas/StatsField"},"phq_attendance_concerts":{"description":"Concerts","$ref":"#/components/schemas/StatsField"},"phq_attendance_conferences":{"description":"Conferences","$ref":"#/components/schemas/StatsField"},"phq_attendance_expos":{"description":"Expos","$ref":"#/components/schemas/StatsField"},"phq_attendance_festivals":{"description":"Festivals","$ref":"#/components/schemas/StatsField"},"phq_attendance_performing_arts":{"description":"Performing Arts","$ref":"#/components/schemas/StatsField"},"phq_attendance_sports":{"description":"Sports","$ref":"#/components/schemas/StatsField"},"phq_attendance_school_holidays":{"description":"School Holidays","$ref":"#/components/schemas/StatsField"},"phq_attendance_community_accommodation":{"description":"Community accommodation 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`2023-10-01`","type":"string","format":"date","title":"Date"},"phq_attendance_academic_graduation":{"$ref":"#/components/schemas/PhqAttendanceFeature"},"phq_attendance_academic_social":{"$ref":"#/components/schemas/PhqAttendanceFeature"},"phq_attendance_community":{"$ref":"#/components/schemas/PhqAttendanceFeature"},"phq_attendance_concerts":{"$ref":"#/components/schemas/PhqAttendanceFeature"},"phq_attendance_conferences":{"$ref":"#/components/schemas/PhqAttendanceFeature"},"phq_attendance_expos":{"$ref":"#/components/schemas/PhqAttendanceFeature"},"phq_attendance_festivals":{"$ref":"#/components/schemas/PhqAttendanceFeature"},"phq_attendance_performing_arts":{"$ref":"#/components/schemas/PhqAttendanceFeature"},"phq_attendance_sports":{"$ref":"#/components/schemas/PhqAttendanceFeature"},"phq_attendance_school_holidays":{"$ref":"#/components/schemas/PhqAttendanceFeature"},"phq_rank_daylight_savings":{"$ref":"#/components/schemas/PhqRankFeature"},"phq_rank_health_warnings":{"$ref":"#/components/schemas/PhqRankFeature"},"phq_rank_observances":{"$ref":"#/components/schemas/PhqRankFeature"},"phq_rank_public_holidays":{"$ref":"#/components/schemas/PhqRankFeature"},"phq_rank_school_holidays":{"$ref":"#/components/schemas/PhqRankFeature"},"phq_rank_politics":{"$ref":"#/components/schemas/PhqRankFeature"},"phq_rank_academic_session":{"$ref":"#/components/schemas/PhqRankFeature"},"phq_rank_academic_exam":{"$ref":"#/components/schemas/PhqRankFeature"},"phq_rank_academic_holiday":{"$ref":"#/components/schemas/PhqRankFeature"},"phq_viewership_sports_american_football":{"$ref":"#/components/schemas/PhqViewershipFeature"},"phq_viewership_sports_auto_racing":{"$ref":"#/components/schemas/PhqViewershipFeature"},"phq_viewership_sports_baseball":{"$ref":"#/components/schemas/PhqViewershipFeature"},"phq_viewership_sports_basketball":{"$ref":"#/components/schemas/PhqViewershipFeature"},"phq_viewership_sports_boxing":{"$ref":"#/components/schemas/PhqViewershipFeature"},"phq_viewership_sports_golf":{"$ref":"#/components/schemas/PhqViewershipFeature"},"phq_viewership_sports_horse_racing":{"$ref":"#/components/schemas/PhqViewershipFeature"},"phq_viewership_sports_ice_hockey":{"$ref":"#/components/schemas/PhqViewershipFeature"},"phq_viewership_sports_mma":{"$ref":"#/components/schemas/PhqViewershipFeature"},"phq_viewership_sports_soccer":{"$ref":"#/components/schemas/PhqViewershipFeature"},"phq_viewership_sports_softball":{"$ref":"#/components/schemas/PhqViewershipFeature"},"phq_viewership_sports_tennis":{"$ref":"#/components/schemas/PhqViewershipFeature"},"phq_viewership_sports_american_football_ncaa":{"$ref":"#/components/schemas/PhqViewershipFeature"},"phq_viewership_sports_american_football_nfl":{"$ref":"#/components/schemas/PhqViewershipFeature"},"phq_viewership_sports_auto_racing_nascar":{"$ref":"#/components/schemas/PhqViewershipFeature"},"phq_viewership_sports_auto_racing_indy_car":{"$ref":"#/components/schemas/PhqViewershipFeature"},"phq_viewership_sports_baseball_ncaa":{"$ref":"#/components/schemas/PhqViewershipFeature"},"phq_viewership_sports_baseball_mlb":{"$ref":"#/components/schemas/PhqViewershipFeature"},"phq_viewership_sports_basketball_ncaa":{"$ref":"#/components/schemas/PhqViewershipFeature"},"phq_viewership_sports_basketball_nba":{"$ref":"#/components/schemas/PhqViewershipFeature"},"phq_viewership_sports_basketball_ncaa_women":{"$ref":"#/components/schemas/PhqViewershipFeature"},"phq_viewership_sports_golf_masters":{"$ref":"#/components/schemas/PhqViewershipFeature"},"phq_viewership_sports_golf_pga_championship":{"$ref":"#/components/schemas/PhqViewershipFeature"},"phq_viewership_sports_golf_us_open":{"$ref":"#/components/schemas/PhqViewershipFeature"},"phq_viewership_sports_golf_pga_tour":{"$ref":"#/components/schemas/PhqViewershipFeature"},"phq_viewership_sports_horse_racing_kentucky_derby":{"$ref":"#/components/schemas/PhqViewershipFeature"},"phq_viewership_sports_horse_racing_preakness_stakes":{"$ref":"#/components/schemas/PhqViewershipFeature"},"phq_viewership_sports_horse_racing_belmont_stakes":{"$ref":"#/components/schemas/PhqViewershipFeature"},"phq_viewership_sports_ice_hockey_nhl":{"$ref":"#/components/schemas/PhqViewershipFeature"},"phq_viewership_sports_mma_ufc":{"$ref":"#/components/schemas/PhqViewershipFeature"},"phq_viewership_sports_soccer_mls":{"$ref":"#/components/schemas/PhqViewershipFeature"},"phq_viewership_sports_soccer_fifa_world_cup_women":{"$ref":"#/components/schemas/PhqViewershipFeature"},"phq_viewership_sports_soccer_fifa_world_cup":{"$ref":"#/components/schemas/PhqViewershipFeature"},"phq_viewership_sports_soccer_uefa_champions_league":{"$ref":"#/components/schemas/PhqViewershipFeature"},"phq_viewership_sports_soccer_concacaf_champions_league":{"$ref":"#/components/schemas/PhqViewershipFeature"},"phq_viewership_sports_soccer_concacaf_gold_cup":{"$ref":"#/components/schemas/PhqViewershipFeature"},"phq_viewership_sports_soccer_copa_america":{"$ref":"#/components/schemas/PhqViewershipFeature"},"phq_viewership_sports_softball_ncaa_women":{"$ref":"#/components/schemas/PhqViewershipFeature"},"phq_viewership_sports_tennis_wimbledon":{"$ref":"#/components/schemas/PhqViewershipFeature"},"phq_viewership_sports_tennis_us_open":{"$ref":"#/components/schemas/PhqViewershipFeature"}},"required":["date"],"title":"PhqFeatureSet"},"PhqAttendanceFeature":{"type":"object","properties":{"stats":{"$ref":"#/components/schemas/FeatureStats"}},"required":["stats"],"title":"PhqAttendanceFeature"},"FeatureStats":{"type":"object","properties":{"count":{"type":"integer","title":"Count"},"sum":{"type":"integer","title":"Sum"},"min":{"type":"integer","title":"Min"},"max":{"type":"integer","title":"Max"},"avg":{"type":"integer","title":"Avg"},"median":{"type":"integer","title":"Median"},"std_dev":{"type":"integer","title":"Std Dev"}},"title":"FeatureStats"},"PhqRankFeature":{"type":"object","properties":{"rank_levels":{"$ref":"#/components/schemas/RankStats"}},"required":["rank_levels"],"title":"PhqRankFeature"},"RankStats":{"type":"object","properties":{"1":{"type":"integer","default":0,"title":"1"},"2":{"type":"integer","default":0,"title":"2"},"3":{"type":"integer","default":0,"title":"3"},"4":{"type":"integer","default":0,"title":"4"},"5":{"type":"integer","default":0,"title":"5"}},"title":"RankStats"},"PhqViewershipFeature":{"type":"object","properties":{"stats":{"$ref":"#/components/schemas/FeatureStats"}},"required":["stats"],"title":"PhqViewershipFeature"},"HTTPValidationError":{"type":"object","properties":{"detail":{"type":"array","items":{"$ref":"#/components/schemas/ValidationError"},"title":"Detail"}},"title":"HTTPValidationError"},"ValidationError":{"type":"object","properties":{"loc":{"type":"array","items":{"anyOf":[{"type":"string"},{"type":"integer"}]},"title":"Location"},"msg":{"type":"string","title":"Message"},"type":{"type":"string","title":"Error Type"}},"required":["loc","msg","type"],"title":"ValidationError"}}},"paths":{"/v1/features/":{"post":{"operationId":"obtain_features_given_criteria_features__post","summary":"Get ML Features","parameters":[{"$ref":"#/components/parameters/OffsetParam"},{"$ref":"#/components/parameters/LimitParam"},{"name":"accept","in":"header","description":"Receive results in JSON or CSV by specifying the appropriate Accept header.\n Supported values:\n\n - application/json\n\n - text/csv","required":false,"schema":{"type":"string","default":"application/json","enum":["application/json","text/csv"],"title":"Accept"}}],"requestBody":{"required":true,"content":{"application/json":{"schema":{"$ref":"#/components/schemas/FeaturesRequestModel"}}}},"responses":{"200":{"description":"Successful Response","content":{"application/json":{"schema":{"$ref":"#/components/schemas/PhqFeaturesResponse"}},"text/csv":{"schema":{"description":"CSV export of results","type":"string"}}}},"422":{"description":"Validation Error","content":{"application/json":{"schema":{"$ref":"#/components/schemas/HTTPValidationError"}}}}},"tags":["Features API"]}}}} ```` ## Available Features {% tabs %} {% tab title="PHQ Attendance Features" %} PHQ Attendance features provide daily-level aggregated stats based on the number of people who we predict will attend events on a given day. This takes into account complications like distributing attendance across multi-day events. {% hint style="success" %} We recommend using impact pattern features instead of generic features if you are in one of the supported industries. See [#attended-events-impact-patterns](#attended-events-impact-patterns "mention"). {% endhint %} **Attended Events Generic Features** Use the generic features in this table if you are not in one of the industries covered by the impact pattern features listed below.
FeatureDescription
phq_attendance_academic_graduationAcademic - Graduation
phq_attendance_academic_socialAcademic - Social
phq_attendance_communityCommunity
phq_attendance_concertsConcerts
phq_attendance_conferencesConferences
phq_attendance_exposExpos
phq_attendance_festivalsFestivals
phq_attendance_performing_artsPerforming Arts
phq_attendance_sportsSports
phq_attendance_school_holidaysSchool Holidays
**Attended Events Impact Pattern Features** Predicted Impact Patterns model the impact of leading days (days before the event), lagging days (days after an event), and the days the event occurs. In the Features API, Impact Patterns are provided as different features with a feature per industry. We have impact pattern features for the accommodation, hospitality (which covers food & beverage including restaurants), and retail industries. The features above are generic features and the features in the table below are the impact pattern features per industry. For example, if you were in the accommodation industry and wanted a feature for the conferences category you'd use `phq_attendance_conferences_accommodation`. {% hint style="success" %} We recommend using impact pattern features instead of generic features if you are in one of the supported industries. See [Predicted Impact Patterns](https://app.gitbook.com/s/tNhzHETmXsrWeVBndqqJ/getting-started/predicthq-data/impact-patterns) {% endhint %}
FeatureDescription
phq_attendance_community_accommodationCommunity accommodation impact
phq_attendance_concerts_accommodationConcerts accommodation impact
phq_attendance_conferences_accommodationConferences accommodation impact
phq_attendance_expos_accommodationExpos accommodation impact
phq_attendance_festivals_accommodationFestivals accommodation impact
phq_attendance_performing_arts_accommodationPerforming Arts accommodation impact
phq_attendance_sports_accommodationSports accommodation impact
phq_attendance_community_hospitalityCommunity hospitality impact
phq_attendance_concerts_hospitalityConcerts hospitality impact
phq_attendance_conferences_hospitalityConferences hospitality impact
phq_attendance_expos_hospitalityExpos hospitality impact
phq_attendance_festivals_hospitalityFestivals hospitality impact
phq_attendance_performing_arts_hospitalityPerforming Arts hospitality impact
phq_attendance_sports_hospitalitySports hospitality impact
phq_attendance_community_retailCommunity Retail impact
phq_attendance_concerts_retailConcerts Retail impact
phq_attendance_conferences_retailConferences Retail impact
phq_attendance_expos_retailExpos Retail impact
phq_attendance_festivals_retailFestivals Retail impact
phq_attendance_performing_arts_retailPerforming Arts Retail impact
phq_attendance_sports_retailSports Retail impact
**Configuration** You can configure PHQ Attendance features using the options below.
FieldDescription
stats
object
optional

You can optionally configure which fields are calculated for each of these features by providing the list of stats fields you would like.

Default fields are count and sum.

Supported fields are:

  • count
  • sum
  • min
  • max
  • avg
  • median
  • std_dev

E.g.

{
  "stats": [
    "count",
    "std_dev",
    "median"
  ]
}
phq_rank
object
optional

Filter for events with a PHQ Rank within a certain range.

Supports the following fields:

  • gt - greater than
  • gte - greater than or equal
  • lt - less than
  • lte - less than or equal

E.g.

{
  "phq_rank": {
    "gt": 50,
    "lt": 80
  }
}
local_rank
object
optional

Filter for events with a Local Rank within a certain range.

Supports the following fields:

  • gt - greater than
  • gte - greater than or equal
  • lt - less than
  • lte - less than or equal

E.g.

{
  "local_rank": {
    "gt": 50,
    "lt": 80
  }
}
{% endtab %} {% tab title="PHQ Impact Features" %} PHQ Impact features provide daily-level aggregated stats based on the predicted impact of an event. This takes into account complications like Impact Patterns (leading and lagging effects of an event). **Holidays and Observances Impact Pattern Features** These features include the Predicted Impact Patterns for public holidays and observances. For example, these features will show when people typically arrive and book accommodation before a holiday and if they tend to leave after the holiday. See [Predicted Impact Patterns](https://app.gitbook.com/s/tNhzHETmXsrWeVBndqqJ/getting-started/predicthq-data/impact-patterns) {% hint style="success" %} We recommend that if you operate in the industries listed below you use the demand impact features for holidays and observances instead of the generic features as these will result in greater forecast accuracy as they include the impact before an event starts and after it finishes. {% endhint %}
FeatureCategoryIndustry
phq_impact_public_holidaysPublic HolidaysN/A
phq_impact_public_holidays_accommodationPublic HolidaysAccomodation
phq_impact_public_holidays_hospitalityPublic HolidaysHospitality/Food & Beverage*
phq_impact_public_holidays_retailPublic HolidaysRetail
phq_impact_observancesObservancesN/A
phq_impact_observances_accommodationObservancesAccomodation
phq_impact_observances_retailObservancesRetail
phq_impact_observances_hospitalityObservancesHospitality/Food & Beverage
phq_impact_school_holidaysSchool HolidaysN/A
phq_impact_school_holidays_retailSchool HolidaysRetail
phq_impact_academic_examAcademicN/A
phq_impact_academic_holidayAcademicN/A
phq_impact_academic_sessionAcademicN/A
**Severe Weather Impact Features** Currently supported industries are: Retail.
FeatureDescriptionIndustry
phq_impact_severe_weather_air_quality_retailSevere Weather - Air QualityRetail
phq_impact_severe_weather_blizzard_retailSevere Weather - BlizzardRetail
phq_impact_severe_weather_cold_wave_retailSevere Weather - Cold Wave - (All)Retail
phq_impact_severe_weather_cold_wave_snow_retailSevere Weather - Cold Wave - SnowRetail
phq_impact_severe_weather_cold_wave_storm_retailSevere Weather - Cold Wave - StormRetail
phq_impact_severe_weather_dust_retailSevere Weather - Dust - (All)Retail
phq_impact_severe_weather_dust_storm_retailSevere Weather - Dust - StormRetail
phq_impact_severe_weather_flood_retailSevere Weather - FloodRetail
phq_impact_severe_weather_heat_wave_retailSevere Weather - Heat WaveRetail
phq_impact_severe_weather_hurricane_retailSevere Weather - HurricaneRetail
phq_impact_severe_weather_thunderstorm_retailSevere Weather - ThunderstormRetail
phq_impact_severe_weather_tornado_retailSevere Weather - TornadoRetail
phq_impact_severe_weather_tropical_storm_retailSevere Weather - Tropical StormRetail
**Attended Events Impact Features** See [#attended-events-impact-pattern-features](#attended-events-impact-pattern-features "mention") **Configuration** You can configure PHQ Impact features using the options below.
FieldDescription
stats
object
optional

You can optionally configure which fields are calculated for each of these features by providing the list of stats fields you would like.

Default fields are count and sum.

Supported fields are:

  • count
  • sum
  • min
  • max
  • avg
  • median
  • std_dev

E.g.

{
  "stats": [
    "count",
    "std_dev",
    "median"
  ]
}
phq_rank
object
optional

Filter for events with a PHQ Rank within a certain range.

Supports the following fields:

  • gt - greater than
  • gte - greater than or equal
  • lt - less than
  • lte - less than or equal

E.g.

{
  "phq_rank": {
    "gt": 50,
    "lt": 80
  }
}
local_rank
object
optional

Filter for events with a Local Rank within a certain range.

Supports the following fields:

  • gt - greater than
  • gte - greater than or equal
  • lt - less than
  • lte - less than or equal

E.g.

{
  "local_rank": {
    "gt": 50,
    "lt": 80
  }
{% endtab %} {% tab title="PHQ Spend Features" %} PHQ Spend features provide daily-level aggregated stats based on total USD we predict will be spent during events on a given day. This takes into account complications like distributing attendance across multi-day events. You can request industry-specific features which are tuned to one of three potential industries: * Accommodation: Event spend relating to hotels and hosts for the purposes of staying at during events. Spend can extend before and after an event actually starts/ends. * Hospitality: Event spend on restaurants, food and drinks. Hotel restaurants are included in this industry. * Transportation: Ground-based transportation for the purposes of getting to and from an event. Includes public and private transport, such as taxis, rails, busses and rideshares.
FeatureDescription
phq_spend_conferencesConferences
phq_spend_exposExpos
phq_spend_sportsSports
phq_spend_communityCommunity
phq_spend_concertsConcerts
phq_spend_festivalsFestivals
phq_spend_performing_artsPerforming Arts
phq_spend_conferences_accommodationConferences - Accommodation
phq_spend_expos_accommodationExpos - Accommodation
phq_spend_sports_accommodationSports - Accommodation
phq_spend_community_accommodationCommunity - Accommodation
phq_spend_concerts_accommodationConcerts - Accommodation
phq_spend_festivals_accommodationFestivals - Accommodation
phq_spend_performing_arts_accommodationPerforming Arts - Accommodation
phq_spend_conferences_hospitalityConferences - Hospitality
phq_spend_expos_hospitalityExpos - Hospitality
phq_spend_sports_hospitalitySports - Hospitality
phq_spend_community_hospitalityCommunity - Hospitality
phq_spend_concerts_hospitalityConcerts - Hospitality
phq_spend_festivals_hospitalityFestivals - Hospitality
phq_spend_performing_arts_hospitalityPerforming Arts - Hospitality
phq_spend_conferences_transportationConferences - Transportation
phq_spend_expos_transportationExpos - Transportation
phq_spend_sports_transportationSports - Transportation
phq_spend_community_transportationCommunity - Transportation
phq_spend_concerts_transportationConcerts - Transportation
phq_spend_festivals_transportationFestivals - Transportation
phq_spend_performing_arts_transportationPerforming Arts - Transportation
**Configuration** You can configure PHQ Spend features using the options below.
FieldDescription
stats
object
optional

You can optionally configure which fields are calculated for each of these features by providing the list of stats fields you would like.

Default fields are count and sum.

Supported fields are:

  • count
  • sum
  • min
  • max
  • avg
  • median
  • std_dev

E.g.

{
  "stats": [
    "count",
    "std_dev",
    "median"
  ]
}
phq_rank
object
optional

Filter for events with a PHQ Rank within a certain range.

Supports the following fields:

  • gt - greater than
  • gte - greater than or equal
  • lt - less than
  • lte - less than or equal

E.g.

{
  "phq_rank": {
    "gt": 50,
    "lt": 80
  }
}
local_rank
object
optional

Filter for events with a Local Rank within a certain range.

Supports the following fields:

  • gt - greater than
  • gte - greater than or equal
  • lt - less than
  • lte - less than or equal

E.g.

{
  "local_rank": {
    "gt": 50,
    "lt": 80
  }
}
{% endtab %} {% tab title="PHQ Viewership Features" %} PHQ Viewership features provide daily-level aggregated stats based on the number of people who we predict will view broadcasts on a given day.
FeatureDescription
phq_viewership_sportsSports - (All)
phq_viewership_sports_american_footballAmerican Football - (All)
phq_viewership_sports_american_football_ncaa_menAmerican Footbal - NCAA Men's
phq_viewership_sports_american_football_nflAmerican Football - NFL
phq_viewership_sports_auto_racingAutomotive Racing - All
phq_viewership_sports_auto_racing_indy_carAutomotive Racing - Indy Car
phq_viewership_sports_auto_racing_nascarAutomotive Racing - NASCAR
phq_viewership_sports_baseballBaseball - (All)
phq_viewership_sports_baseball_mlbBaseball - MLB
phq_viewership_sports_baseball_ncaa_menBaseball - NCAA Men's
phq_viewership_sports_basketballBasketball - (All)
phq_viewership_sports_basketball_nbaBasketball - NBA
phq_viewership_sports_basketball_ncaa_menBasketball - NCAA Men's
phq_viewership_sports_basketball_ncaa_womenBasketball - NCAA Women's
phq_viewership_sports_boxingBoxing - (All)
phq_viewership_sports_golfGolf - (All)
phq_viewership_sports_golf_mastersGolf - Masters
phq_viewership_sports_golf_pga_championshipGolf - PGA Championships
phq_viewership_sports_golf_pga_tourGolf - PGA Tours
phq_viewership_sports_golf_us_openGolf - US Open
phq_viewership_sports_horse_racingHorse Racing - (All)
phq_viewership_sports_horse_racing_belmont_stakesHorse Racing - Belmont Stakes
phq_viewership_sports_horse_racing_kentucky_derbyHorse Racing - Kentucky Derby
phq_viewership_sports_horse_racing_preakness_stakesHorse Racing - Preakness Stakes
phq_viewership_sports_ice_hockeyIce Hockey - (All)
phq_viewership_sports_ice_hockey_nhlIce Hockey - NHL
phq_viewership_sports_mmaMixed Martial Arts - (All)
phq_viewership_sports_mma_ufcMixed Martial Arts - UFC
phq_viewership_sports_soccerSoccer - (All)
phq_viewership_sports_soccer_concacaf_champions_leagueSoccer - CONCACAF Champions League
phq_viewership_sports_soccer_concacaf_gold_cupSoccer - CONCACAF Gold Cup
phq_viewership_sports_soccer_copa_america_menSoccer - COPA America Men's
phq_viewership_sports_soccer_fifa_world_cup_womenSoccer - FIFA World Cup Women's
phq_viewership_sports_soccer_fifa_world_cup_menSoccer - FIFA World Cup Men's
phq_viewership_sports_soccer_mlsSoccer - MLS
phq_viewership_sports_soccer_uefa_champions_league_menSoccer - UEFA Champions League Men's
phq_viewership_sports_softballSoftball - (All)
phq_viewership_sports_softball_ncaa_womenSoftball - NCAA Women's
phq_viewership_sports_tennisTennis - (All)
phq_viewership_sports_tennis_us_openTennis - US Open
phq_viewership_sports_tennis_wimbledonTennis - Wimbledon
**Configuration** You can configure PHQ Attendance features using the options below.
FieldDescription
stats
object
optional

You can optionally configure which fields are calculated for each of these features by providing the list of stats fields you would like.

Default fields are count and sum.

Supported fields are:

  • count
  • sum
  • min
  • max
  • avg
  • median
  • std_dev

E.g.

{
  "stats": [
    "count",
    "std_dev",
    "median"
  ]
}
phq_rank
object
optional

Filter for events with a PHQ Rank within a certain range.

Supports the following fields:

  • gt - greater than
  • gte - greater than or equal
  • lt - less than
  • lte - less than or equal

E.g.

{
  "phq_rank": {
    "gt": 50,
    "lt": 80
  }
}
local_rank
object
optiona

Filter for events with a Local Rank within a certain range.

Supports the following fields:

  • gt - greater than
  • gte - greater than or equal
  • lt - less than
  • lte - less than or equal

E.g.

{
  "local_rank": {
    "gt": 50,
    "lt": 80
  }
}
{% endtab %} {% tab title="PHQ Rank Features" %} PHQ Rank features provide the daily-level aggregated sum of events bucketed by PHQ Rank level (1-5). **PHQ Rank Impact Pattern Features** See the "Holidays and Observances Impact Pattern Features" under PHQ Impact in the tab above. These features cover the Accommodation, Retail, and Hospitality (Food & Beverage) industries. {% hint style="success" %} We recommend that if you operate in the supported industries you use the demand impact features for holidays and observances instead of the generic features as these will result in greater forecast accuracy as they include the impact before an event starts and after it finishes. {% endhint %} **PHQ Rank Generic Features** These are generic features that do not include Predicted Impact Patterns and should be used if you are *not* in one of the industries that we have impact patterns for.
FeatureDescription
phq_rank_observancesObservances
phq_rank_public_holidaysPublic Holidays
phq_rank_school_holidaysSchool Holidays
phq_rank_academic_sessionAcademic - Session
phq_rank_academic_examAcademic - Exam
phq_rank_academic_holidayAcademic - Holiday
phq_rank_daylight_savingsDaylight savings
phq_rank_health_warningsHealth Warnings
phq_rank_politicsPolitics
**Configuration** PHQ Rank features cannot currently be configured further. When requesting `phq_rank_*` features set the value as `true` indicating you require the default calculations. {% endtab %} {% endtabs %} ## Feature Response Fields Other than the date, the structure of each result here will depend on how you configured the feature in your request and the type of feature. {% tabs %} {% tab title="PHQ Attendance Features" %}
FieldDescription
date
string		Date in local time.

E.g. 2023-10-01
<phq_attendance_*>
object

Daily-level feature result. The structure of the result here will depend on how you configured the feature in your request.

PHQ Attendance features are stats-based.

Default fields are count and sum.

E.g.

{
  "stats": {
    "count": 5,
    "sum": 17307,
    "min": 1000,
    "max": 9215,
    "avg": 3461.4,
    "median": 2620.0,
    "std_dev": 2978.810473997968
  }
}
{% endtab %} {% tab title="PHQ Impact Features" %}
FieldDescription
date
string		Date in local time.

E.g. 2023-10-01
<phq_impact_*>
object

Daily-level feature result. The structure of the result here will depend on how you configured the feature in your request.

PHQ Impact features are stats-based.

Default fields are count and sum.

E.g.

{
  "stats": {
    "count": 5,
    "sum": 17307,
    "min": 1000,
    "max": 9215,
    "avg": 3461.4,
    "median": 2620.0,
    "std_dev": 2978.810473997968
  }
}
{% endtab %} {% tab title="PHQ Rank Features" %}
FieldDescription
date
string		Date in local time.

E.g. 2023-10-01
<phq_rank_*>
object

Daily-level feature result. The structure of the result here is always the same as PHQ Rank features cannot currently be configured.

Will contain a rank_levels field which indicates the sum of matching events active on the date at each PHQ Rank level.

PHQ Rank is on a scale of 0 to 100 and the levels are bucketed as:

  • 1 - Minor (rank between 0 and 20).
  • 2 - Moderate (rank between 21 and 40).
  • 3 - Important (rank between 41 and 60).
  • 4 - Significant (rank between 61 and 80).
  • 5 - Major (rank between 81 and 100).

E.g.

{
  "rank_levels": {
    "1": 0,
    "2": 0,
    "3": 0,
    "4": 2,
    "5": 0
  }
}
{% endtab %} {% tab title="PHQ Spend Features" %}
FieldDescription
date
string		Date in local time.

E.g. 2023-10-01
<phq_spend_*>
object

Daily-level feature result. The structure of the result here will depend on how you configured the feature in your request.

PHQ Spend features are stats-based.

Default fields are count and sum.

E.g.

{
  "stats": {
    "count": 5,
    "sum": 17307,
    "min": 1000,
    "max": 9215,
    "avg": 3461.4,
    "median": 2620.0,
    "std_dev": 2978.810473997968
  }
}
{% endtab %} {% tab title="PHQ Viewership Features" %}
FieldDescription
date
string		Date in local time.

E.g. 2023-10-01
<phq_viewership_*>
object

Daily-level feature result. The structure of the result here will depend on how you configured the feature in your request.

PHQ Viewership features are stats-based.

Default fields are count and sum.

E.g.

{
  "stats": {
    "count": 5,
    "sum": 17307,
    "min": 1000,
    "max": 9215,
    "avg": 3461.4,
    "median": 2620.0,
    "std_dev": 2978.810473997968
  }
}
{% endtab %} {% endtabs %} ## Examples {% tabs %} {% tab title="curl" %} ```bash curl -X POST "https://api.predicthq.com/v1/features/?offset=0&limit=100" \ -H "Accept: application/json" \ -H "Authorization: Bearer $ACCESS_TOKEN" \ --data @<(cat <