Blog - Anomaly

Jan 30

Salesforce Anomaly Detection Using Anomaly.io

detect anomalies in salesforce

Monitoring Key Performance Indicators (KPIs) is essential to running a successful business. As one example, you should examine your lead generation KPI several times a day, to allow you to detect and correct problems as quickly as possible.

But you’re busy—you don’t have time to watch KPI indicators all day long. That’s where Anomaly.io comes in. By combining our detection algorithms with your Salesforce data, you can automatically detect problems and notify the appropriate personnel to ensure that speedy corrective action is taken.

Jan 25

Detecting Anomalies in Correlated Time Series

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anomaly in time series correlation

Monitoring key performance indicators (KPIs), sales or any other product data means working within an ecosystem where very often you will see metrics correlating with each other. When a normal correlation between two metrics is broken, we have reason to suspect something strange is happening.

As an example, take a look at anomaly.io analytics during its early days (a long time ago). In the graphic above, the new users are shown in green and the returning users in red. Clearly, something strange happens in the middle of November. Let’s use some techniques to find out more!
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Mar 10

Detecting Correlation Among Multiple Time Series

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detect-correlation

To determine the level of correlation between various metrics we often use the normalized cross-correlation formula.2
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Mar 08

Understanding Cross-Correlation, Auto-Correlation, Normalization and Time Shift

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normalized-cross-correlation-auto-shift

To detect the correlation of time series we often use auto-correlation, cross-correlation or normalized cross-correlation. Let’s study these techniques to understand them better.
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Feb 08

Change Point Detection with Seasonal Time Series

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change-point-seasonality

Previously, we looked at using Twitter Breakout (EDM) to detect Anomalies. As with the popular E-Divisive, EDM detects mean shift and changes in distribution. Both algorithms work with seasonal time series, but perform even better without seasonality. Read More

Jan 20

Anomaly Detection with Twitter Breakout

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AnomalyDetectionBreakout

We previously tested Twitter Anomaly Detection package using the R language. Now let’s take a look at Twitter Breakout Detection.

Jan 12

Detecting Anomalies with Moving Median Decomposition

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anomaly-decomposition-median

Time series decomposition splits a time series into seasonal, trend and random residual time series. The trend and the random time series can both be used to detect anomalies. But detecting anomalies in an already anomalous time series isn’t easy.
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Dec 03

How to Seasonally Adjust a Time Series in R

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seasonal-adjustment

What is seasonal adjustment? Why is it important, and when should we use it? How should it be done? In this article, we will answer these and many other questions related to seasonality in data.
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Dec 01

Extracting Seasonality and Trend from Data: Decomposition Using R

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time-series-decomposition-seasonal-trend

Time series decomposition works by splitting a time series into three components: seasonality, trends and random fluctiation. To show how this works, we will study the decompose( ) and STL( ) functions in the R language.
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Nov 26

Moving Median is Robust to Anomalies

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moving-median

It can be difficult to detect the underlying trend of a time series in the presence of anomalies, due to unwanted noise. Fortunately, there are techniques to take into account those anomalies, so you can work with this kind of time series. One of them is the moving median.