Impact Patterns

Also known as “Demand impact patterns”. This field shows the impact for leading days (days before the event), lagging days (days after an event), and the days the event occurs.

You can use Demand Impact Patterns in your demand forecasting so that your machine learning models will take account of the impact on leading and lagging days. In our testing we have found using impact patterns increases forecasting accuracy.

Vertical, type, and category mapping

Impact Patterns are available for the following industry segments and categories:

Vertical	Impact Type	Category
retail	phq_rank	severe-weather
accommodation	phq_attendance	community
		concerts
		conferences
		expos
		festivals
		performing-arts
		sports
hospitality	phq_attendance	community
		concerts
		conferences
		expos
		festivals
		performing-arts
		sports

Impact Patterns in the Events API

Impact patterns are returned in the Events API response in the impact_patterns field. This shows the Impact Pattern for each event. Below are the details of the data structure of that field.

impact_patterns is an array of impact pattern objects. The same event can have different impact patterns for different industry verticals. It contains the following fields:

• vertical - The industry vertical the impact pattern applies to.

• impact_type - Indicates the type of impact shown in the impact pattern. This will apply to either phq_rank or phq_attendance, depending on the vertical.

impacts is an array of objects with one entry for each day that contains the following values:

• date_local - the date in the local timezone of the event.

• value - the value of the impact_type for that given day. For example, if the impact_type was phq_rank the value would be the PHQ Rank value on the given day. In the case for accommodation or hospitality where the impact_type is phq_attendance, this is what will be presented in this field.

• position - can be leading, event_day or lagging. leading are the days before the event occurs, event_day are the days the event occurs, and lagging are the days after the event has occurred.

"impact_patterns": [
    {
        "vertical": "accommodation",
        "impact_type": "phq_attendance",
        "impacts": [
            {"date_local": "2018-02-26", "value": 14250, "position": "leading"},
            {"date_local": "2018-02-27", "value": 14250, "position": "leading"},
            {"date_local": "2018-02-28", "value": 85500, "position": "leading"},
            {"date_local": "2018-03-01", "value": 95000, "position": "event_day"},
            {"date_local": "2018-03-02", "value": 95000, "position": "event_day"},
            {"date_local": "2018-03-03", "value": 95000, "position": "event_day"},
            {"date_local": "2018-03-04", "value": 95000, "position": "event_day"},
            {"date_local": "2018-03-05", "value": 33250, "position": "lagging"},
            {"date_local": "2018-03-06", "value": 4750, "position": "lagging"},
        ],
    }, {
        "vertical": "hospitality",
        "impact_type": "phq_attendance",
        "impacts": [
            {"date_local": "2018-02-26", "value": 16151, "position": "leading"},
            {"date_local": "2018-02-27", "value": 40850, "position": "leading"},
            {"date_local": "2018-02-28", "value": 40850, "position": "leading"},
            {"date_local": "2018-03-01", "value": 95000, "position": "event_day"},
            {"date_local": "2018-03-02", "value": 95000, "position": "event_day"},
            {"date_local": "2018-03-03", "value": 95000, "position": "event_day"},
            {"date_local": "2018-03-04", "value": 95000, "position": "event_day"},
            {"date_local": "2018-03-05", "value": 14250, "position": "lagging"},
        ],
    }
]

or for retail for severe weather events

"impact_patterns": [
    {
        "vertical": "retail",
        "impact_type": "phq_rank",
        "imputed_impact_pattern": true,
        "impact_range_start_dt": "2018-02-03T15:00:00+00:00",
        "impact_range_end_dt": "2018-02-07T14:59:59+00:00",
        "impacts": [
            {"date_local": "2018-02-04", "value": 64, "position": "event_day"},
            {"date_local": "2018-02-05", "value": 64, "position": "event_day"},
            {"date_local": "2018-02-06", "value": 64, "position": "event_day"},
            {"date_local": "2018-02-07", "value": 64, "position": "event_day"},
        ],
    }
]

Impact Patterns in the Features API

You can also use Demand Impact Patterns with the Features API. The features API provides pre-built machine learning features for demand forecasting. See the features API documentation. Use the features for your industry to get more accurate forecasting results. We have a generic feature without impact patterns for sports called phq_attendance_sports but that does not include impact patterns so only shows the impact on the days of the event. In order to use impact patterns with the features API you need to use the impact pattern features. For example, if you are in the accommodation segment and are using the features API to find the impact of sports events on your location you would use phq_attendance_sports_accommodation. If you were in the Hospitality Segment you would use phq_attendance_sports_hospitality.