In the last post, we were able to identify when a regime change occurs. Today we will focus on speed (well, a trade-off)
For the next question, we will still be using the datasets available at https://github.com/matrix-profile-foundation/mpf-datasets so you can try this at home.
The original code (MATLAB) and data are here.
Now let’s start:
How do I quickly search this long dataset for patterns, if an approximate search is acceptable?

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In the last post, we were able to identify when a regime change occurs. Today we will focus on speed (well, a trade-off)
For the next question, we will still be using the datasets available at https://github.com/matrix-profile-foundation/mpf-datasets so you can try this at home.
The original code (MATLAB) and data are here.
Now let’s start:
How do I quickly search this long dataset for patterns, if an approximate search is acceptable?

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In the last post took a very long time series, and we summarize it. Now we will do something that seems related when we look at the regime bar: regime change detection.
For the next question, we will still be using the datasets available at https://github.com/matrix-profile-foundation/mpf-datasets so you can try this at home.
The original code (MATLAB) and data are here.
Now let’s start:
When does the regime change in this time series?

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In the last post we managed to find similar patterns between two time series.
For the next question, we will still be using the datasets available at https://github.com/matrix-profile-foundation/mpf-datasets so you can try this at home.
The original code (MATLAB) and data are here.
Now let’s start:
If you had to summarize this long time series with just two shorter examples, what would they be? This is a new kind of question.

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In the last post we’ve understood and find Discords in our data.
For the next question, we will still be using the datasets available at https://github.com/matrix-profile-foundation/mpf-datasets so you can try this at home.
The original code (MATLAB) and data are here.
Now let’s start:
Is there any pattern that is common to these two time series? Now we will see one of the most interesting and fast jobs that the Matrix Profile can do (there are more, for sure).

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In the last post we started looking for repeated patterns in a time series, what we call Motifs.
For the next question, we will still be using the datasets available at https://github.com/matrix-profile-foundation/mpf-datasets so you can try this at home.
The original code (MATLAB) and data are here.
Now let’s start:
What are the three most unusual days in this three-month-long dataset? Now we don’t know what we are looking for, but we want to discover something.

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In the last post we started looking for a known pattern in a time series.
For the next question, we will still be using the datasets available at https://github.com/matrix-profile-foundation/mpf-datasets so you can try this at home.
The original code (MATLAB) and data are here.
Now let’s start:
Are there any repeated patterns in my data? Now we don’t know what we are looking for, but we want to discover something.

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I decided to start this series of Time Series Data Mining base on Eamonn’s presentation, so that’s why the title is “100”. That’s the idea, but for now, we only have 19 questions ready to go.
I’ll use the datasets available at https://github.com/matrix-profile-foundation/mpf-datasets so you can try this at home.
The original code (MATLAB) and data are here..
So, let’s start with number one:
Have we ever seen a pattern that looks just like this?

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Since the beginning of the tsmp package, it was evident that a series of algorithms around the Matrix Profile would pop-up sooner or later.
After the creation of the Matrix Profile Foundation (MPF), the tsmp package had doubled the number of monthly downloads, and that is a good thing!
The current version of tsmp, as shown in the previous post had added the new Pan-Matrix Profile and introduced the Matrix Profile API that aims to standardize high-level tools across multiple programming languages.

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A new tool for painlessly analyzing your time series.
We’re surrounded by time-series data. From finance to IoT to marketing, many organizations produce thousands of these metrics and mine them to uncover business-critical insights. A Site Reliability Engineer might monitor hundreds of thousands of time series streams from a server farm, in the hopes of detecting anomalous events and preventing catastrophic failure. Alternatively, a brick and mortar retailer might care about identifying patterns of customer foot traffic and leveraging them to guide inventory decisions.

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