PredictHQ Beam

The automated correlation engine to accurately reveal the events that drive demand for your business.

12 Jan 2020
23
-116
185K
3 feb 2020
250
189
264K
The input

Combine unstructured event data + transactional data

PredictHQ converts unstructured, dynamic event data into a workable dataset, allowing data science teams to use intelligent event data in machine learning models. PredictHQ Beam combines and correlates our event data with your transactional demand data.

01

Impute Values + Check Quality

Beam automatically detects missing values from your transactional data and imputes values using our advanced time series reconstruction technologies. It then systematically checks data quality with stationary attributes and rolling standard deviation.
02

Time Series Decomposition

We decompose your time series transactional data using IteSSA nonparametric machine learning algorithms to automatically detect weekly and monthly cyclicities, seasonal patterns, and long term and short term trends.
03

Anomaly Detection

Using the patterns identified in step two, Beam automatically detects the incremental anomalies in your transactional data based on the re-constructed demand patterns.
04

False-positive Detection

False positive detection algorithms detect the positive anomalies which are not generated by attendance-based events and then adjust the incremental demand estimation.
05

Determine Incremental Demand + Correlate To Events

PredictHQ uses FDR (false discovery rate) to calculate the strength level of the incremental demand. We segment all the detected incremental demands into strong, medium, weak and no impacts. We join the detected incremental demands with attendance-based events impacts as well as holiday events counts to correlate the time series transactional data with events.
The Output

Export the results and seamlessly integrate them into your prediction models

Download the full results to understand which events impact your demand and use Aggregate Event Impact to improve your forecasting models.

Demand graph
High-impact events list
Custom PDF report
12 Jan 2020
23
-116
185K
3 feb 2020
250
189
264K

Easily visualize and scroll over your demand signals and view corresponding event impact in Control Center.

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