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Added Binder links to top of tutorials
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‎docs/Tutorial_AB_Joins.ipynb‎

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"cell_type": "markdown",
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"# Finding Conserved Patterns Across Two Time Series \n",
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"# Finding Conserved Patterns Across Two Time Series\n",
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"\n",
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"[![Binder](https://mybinder.org/badge_logo.svg)](https://mybinder.org/v2/gh/TDAmeritrade/stumpy/main?filepath=notebooks/Tutorial_AB_Joins.ipynb)\n",
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"## AB-Joins\n",
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"name": "python",
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"nbconvert_exporter": "python",
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‎docs/Tutorial_Consensus_Motif.ipynb‎

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"source": [
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"# Consensus Motif Search\n",
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"\n",
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"[![Binder](https://mybinder.org/badge_logo.svg)](https://mybinder.org/v2/gh/TDAmeritrade/stumpy/main?filepath=notebooks/Tutorial_Consensus_Motif.ipynb)\n",
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"\n",
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"This tutorial utilizes the main takeaways from the [Matrix Profile XV paper](https://www.cs.ucr.edu/~eamonn/consensus_Motif_ICDM_Long_version.pdf).\n",
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"\n",
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"[Matrix profiles](https://stumpy.readthedocs.io/en/latest/Tutorial_The_Matrix_Profile.html) can be used to [find conserved patterns within a single time series](https://stumpy.readthedocs.io/en/latest/Tutorial_STUMPY_Basics.html) (self-join) and [across two time series](https://stumpy.readthedocs.io/en/latest/Tutorial_AB_Joins.html) (AB-join). In both cases these conserved patterns are often called \"motifs\". And, when considering a set of three or more time series, one common trick for identifying a conserved motif across the entire set is to: \n",
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"name": "python",
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‎docs/Tutorial_Fast_Approximate_Matrix_Profiles.ipynb‎

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"source": [
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"# Fast Approximate Matrix Profiles with SCRUMP\n",
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"\n",
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"[![Binder](https://mybinder.org/badge_logo.svg)](https://mybinder.org/v2/gh/TDAmeritrade/stumpy/main?filepath=notebooks/Tutorial_Fast_Approximate_Matrix_Profiles.ipynb)\n",
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"\n",
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"In \n",
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"[this paper](https://www.cs.ucr.edu/~eamonn/SCRIMP_ICDM_camera_ready_updated.pdf), a new approach called \"SCRIMP++\", which computes a matrix profile in an incremental fashion, is presented. When only an approximate matrix profile is needed, the this algorithm uses certain properties of the matrix profile calculation to greatly reduce the total computational time and, in this tutorial, we'll demonstrate how this approach may be sufficient for your applications.\n",
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‎docs/Tutorial_Matrix_Profiles_For_Streaming_Data.ipynb‎

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"source": [
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"# Incremental Matrix Profiles for Streaming Time Series Data\n",
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"[![Binder](https://mybinder.org/badge_logo.svg)](https://mybinder.org/v2/gh/TDAmeritrade/stumpy/main?filepath=notebooks/Tutorial_Matrix_Profiles_For_Streaming_Data.ipynb)\n",
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"Now that you have a basic understanding of how to compute a matrix profile, in this short tutorial, we will demonstrate how to incrementally update your matrix profile when you have streaming (on-line) data using the `stumpy.stumpi` (\"STUMP Incremental\") function. You can learn more about the details of this approach by reading Section G of the [Matrix Profile I](https://www.cs.ucr.edu/~eamonn/PID4481997_extend_Matrix%20Profile_I.pdf) paper and Section 4.6 and Table 5 [this paper](https://www.cs.ucr.edu/~eamonn/ten_quadrillion.pdf)."
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‎docs/Tutorial_Multidimensional_Motif_Discovery.ipynb‎

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"# Multidimensional Motif Discovery\n",
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"[![Binder](https://mybinder.org/badge_logo.svg)](https://mybinder.org/v2/gh/TDAmeritrade/stumpy/main?filepath=notebooks/Tutorial_Multidimensional_Motif_Discovery.ipynb)\n",
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"\n",
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"## Finding a Motif in Multidimensional Time Series Data with MSTUMP\n",
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"\n",
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"This tutorial utilizes the main takeaways from the [Matrix Profile VI](https://www.cs.ucr.edu/~eamonn/Motif_Discovery_ICDM.pdf) research paper and requires STUMPY v1.6.1 or newer. Also, the word “dimensionality” is overloaded for multi-dimensional time series since it is often used to refer to both the number of time series and to the number of data points in a subsequence. For clarity, we restrict our use of \"dimensions\" to refer only to the number of time series and not to the number of data points.\n",
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‎docs/Tutorial_STUMPY_Basics.ipynb‎

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"# STUMPY Basics\n",
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"[![Binder](https://mybinder.org/badge_logo.svg)](https://mybinder.org/v2/gh/TDAmeritrade/stumpy/main?filepath=notebooks/Tutorial_STUMPY_Basics.ipynb)\n",
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"## Analyzing Motifs and Anomalies with STUMP\n",
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"\n",
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"This tutorial utilizes the main takeaways from the research papers: [Matrix Profile I](http://www.cs.ucr.edu/~eamonn/PID4481997_extend_Matrix%20Profile_I.pdf) & [Matrix Profile II](http://www.cs.ucr.edu/~eamonn/STOMP_GPU_final_submission_camera_ready.pdf).\n",
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‎docs/Tutorial_Semantic_Segmentation.ipynb‎

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"# Semantic Segmentation \n",
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"[![Binder](https://mybinder.org/badge_logo.svg)](https://mybinder.org/v2/gh/TDAmeritrade/stumpy/main?filepath=notebooks/Tutorial_Semantic_Segmentation.ipynb)\n",
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"## Analyzing Arterial Blood Pressure Data with FLUSS and FLOSS\n",
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"This example utilizes the main takeaways from the [Matrix Profile VIII](https://www.cs.ucr.edu/~eamonn/Segmentation_ICDM.pdf) research paper. For proper context, we highly recommend that you read the paper first but know that our implementations follow this paper closely.\n",
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‎docs/Tutorial_Time_Series_Chains.ipynb‎

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"[![Binder](https://mybinder.org/badge_logo.svg)](https://mybinder.org/v2/gh/TDAmeritrade/stumpy/main?filepath=notebooks/Tutorial_Time_Series_Chains.ipynb)\n",
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"## Forecasting Web Query Data with Anchored Time Series Chains (ATSC)\n",
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"This example is adapted from the [Web Query Volume case study](http://www.www2015.it/documents/proceedings/proceedings/p721.pdf) and utilizes the main takeaways from the [Matrix Profile VII](https://www.cs.ucr.edu/~eamonn/chains_ICDM.pdf) research paper."
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‎docs/tutorials.rst‎

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Tutorials
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=========
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.. image:: https://mybinder.org/badge_logo.svg
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:target: https://mybinder.org/v2/gh/TDAmeritrade/stumpy/main?filepath=notebooks
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:alt: Binder
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.. toctree::
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:glob:

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