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1. SAX-Implementation-on-UCR-TS SAX-Implementation-on-UCR-TS Public

   Implementing Symbolic Aggregate approXimation to find the error rate and accuracy on the UCR timeseries datasets (https://www.cs.ucr.edu/~eamonn/time_series_data/).

   Python 5

2. Time-Series-classification-using-feature-extraction Time-Series-classification-using-feature-extraction Public

   Using Dynamic time warping distances as features for improved time series classification. We use DTW distances between time series as features and then predict the labels.

   Python 5

3. Error-rates-on-UCR-TS Error-rates-on-UCR-TS Public

   To compute error rates on UCR time series Data sets and to compare them within. This has 1NN ED, 1NN DTW, 1NN DTW(r),BOSS,SAX,BOSSVS and WEASEL Algorithms.

   Python 1

4. Sensor_SVM Sensor_SVM Public

   Time series classification using DTW distances as a feature, LIBSVM and WEASEL methods, on the Robosensor data.

   Jupyter Notebook 1 1

5. DTW-Example DTW-Example Public

   Example of DTW algorithm's working.

   Jupyter Notebook 1

6. Indian_liver_patient Indian_liver_patient Public

   The data set consists of the data of liver diseased patients from the Indian subcontinent, and this helps building a ML model using Random Forest and Logistic regression to predict the diseased.

   Jupyter Notebook 1