July 2015 Newsletter: Announcing HTM for Stocks - Open Source Example HTM App
July 02, 2015
Dear Numenta newsletter subscriber:
Over the last two years, we have created a series of example applications that illustrate the capabilities of HTM. Grok for IT Analytics, available for download on the Amazon Web Services Marketplace, uses HTM to detect anomalies in AWS server metrics. We have published source code for several other example applications that use HTM to detect anomalies in human behavior, and in GPS data. But up until now it hasn’t been easy for an individual to get a sense of how HTM-based anomaly detection works; you either need to bring up an AWS server instance, or put in a reasonable amount of software development effort.
So we have created a new example application, called HTM for Stocks, which makes it much easier for you to experience how HTM detects anomalies. HTM for Stocks is available for free here: Download HTM for Stocks. At this point, the application is available only for an Android mobile device.
HTM for Stocks applies HTM modeling and anomaly detection to 200 large capitalization public companies. The application automatically models three data streams for each stock: stock volume, stock price, and Twitter volume. It figures out “normal” for each of these data streams for each company, and then lets you know if something abnormal has occurred.
Here is what we hope you will notice when using HTM for Stocks:
HTM enables automatic modeling of many models here, we are creating 600 separate models (3 for each of 200 companies). No human intervention is required to adjust parameters or tune models.
HTM models learn continuously, with each new data point. If a company changes its fundamentals, taking its stock volume to a new level, at first it will show an anomaly, but after a short period, it will learn the “new normal”.
The models are ranked by the most anomalous to the least anomalous. If you scroll down, you will note that many of the stocks show no anomalies. This ranking of anomalies allows you to focus on the companies that are the most different from the norm.
Some of the anomalies detected by HTM for Stocks may appear obvious but others are subtle and not easily detected by a human. For example, if you watch stock volumes, you will see that there often is a spike in the beginning and at the end of the trading day. It will notice if those spikes continue longer than normal for that particular stock. Such examples demonstrate the power of finding temporal patterns
When you see an anomaly, it’s very informative to look at the Twitter stream for the corresponding time frame. There, you often will be able to quickly determine the reason for the anomaly, such as an earnings announcement, a takeover bid, a lawsuit, or a rapidly growing interest in something the company did.
In keeping with our focus as a technology provider, we do not intend to build HTM for Stocks into a full commercial application, and so we have provided the source code for HTM for Stocks alongside our NuPIC open source project at https://github.com/numenta/numenta-apps, available under a AGPL v3 license*. Developers may find that HTM for Stocks code can be used to create derivative products that track other data streams. We also welcome partners who are interested in a commercial license to the HTM for Stocks code; in this case please write to email@example.com.
We hope that you will enjoy using HTM for Stocks. We welcome your feedback at firstname.lastname@example.org.
*This content has been updated to reflect our new AGPL license.