Quick Insights for Single Time-Series Data

Intended audience: END-USERS DATA SCIENCE DEVELOPERS

AO Easy Answers: 4.3

Overview

Quick Insights can be generated at runtime by the user from Apps on the Easy Answers Results page or from a scheduled task created by the Solution Developer using the Insight Composer in the AO Platform. This topic covers the Quick Insights relating to Single Time-Series Data.

Single Time-Series Quick Insights (Bayesian)

Outliers - Outliers are data points that do not follow the trend of the rest of the data. This insight identifies the points that stand out.
Change Points - When values in a time series suddenly and significantly change from the previous trend, it is called a change point. This insight identifies the different change points in the time series. Such abrupt changes may represent transitions that occur between states.
Trend Line - Values in a time series may trend upwards or downwards over time, or may have repeating patterns that occur at regular intervals. This insight identifies the direction and slope of a linear trend in the data over time.
Periodic Estimation - This insight is generated as part of the Trend Line Quick Insight. The output, however, is summarized for specific time durations, such as hours in a day, days in a week, months in a year, etc…
Forecast - Through time series analysis, the forecasting insight identifies the evolving future trend based on the past behavior of the time series data.

Configuring Single Time-Series Quick Insights

The Single Time-Series Quick Insights have a number of Common Properties as well as a few additional unique properties per Quick Insight. When this Quick Insight is selected, the user can configure all the properties, or leave the default configuration as is and just decide on which individual Quick Insights should be added, eg. Outliers, Change Points, Trend Line, and/or Forecast.

For the Single Time-Series Quick Insights using Exogenous Variables impacting Forecast, see Using Exogenous Variables.

Properties

Label	UI	Default	Description
Model Size	Dropdown	Default (2000)	Manage the model’s size. Larger models provide reliable interpretations at the cost of longer insight run times. Smaller models produce quicker results but lack the inclusiveness of larger counterparts. Default - Samples: 2000. Custom - enter a value in the Number of Trials field below.
Number of Trials	Text Field	2000	Enter a number for the number of Trial Samples. This field is only visible if the user has selected Custom in the Model Size field above.
Type of Data	Dropdown	Data is Normal	Select the Type of Data from either of the following options: Data is Normal - data is expected to have a normal value distribution. Data has Outliers - data is expected to have a distribution with some outliers.
Analyze By	Dropdown	Month	Time durations for which the data will be analyzed. Multi-select (tags) for one or more time duration values, including: Hour Day Week Month Quarter Year
Show Analysis for Periodic Estimation	On/Off Toggle(s)		One or more On/Off Toggles will show depending on the selection for Analyze By above.
Special Day Insight	On/Off Toggle	Off	Will generate additional insights relating specifically to configured Special Days for the Ontology. This toggle is only available if Day is selected for Analyze By above.
Change Point Selection	Dropdown	Automatic Change Points	Select how Change Points will help the model adjust to significant shifts in business trends, including: Automatic Change Points (default) - the system detects and adjusts to trend shifts automatically. Example: A surge in online orders during Black Friday. Candidate Change Points - the system suggests potential shifts, allowing review before applying. Example: The model highlights seasonal sales patterns for review. Custom Change Points - Define trend shifts manually based on business knowledge. Example: Marking a product launch date that impacts sales growth.
Number of Candidate Change Points	Number Field		Defines how many key trend shifts the system considers when analyzing business data. This field only shows when the Candidate Change Points value is selected in the Change Point Selection above. A higher number captures more variations, but may lead to unnecessary complexity. Example: Tracking daily fluctuations in stock prices. A lower number keeps insights simpler, focusing only on major changes. Example: Identifying major sales peaks in a quarterly revenue report.
Select a Date(s)/Range	Date Field(s)		Defines specific Date(s)/Range for Change Points. This field only shows when the Custom Change Points value is selected in the Change Point Selection above.
Missing Values (%)	Text Field	25	Represents the percentage of missing data in the time series, helping assess data quality. It also defines the limit for how much missing data can be imported. A higher threshold allows the model to tolerate and fill in more missing values, which can impact forecast accuracy.

Summary of Quick Insights

	Single Time-Series
Number of Series	Should have 1 time series
Type of Data	Continuous or Discrete data
Charts Examples	Time-Series Chart
Examples	Time series of a single stock's data will give insights into trend, seasonality, outlier, and changepoint detection.

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