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+# AEP SSL
+
+## Overview
+
+Introducing self supervised learning for time series data, down stream task can be susbsequently used posterior to training an SSL model, so its
+not dependent on classification/regression/forecasting etc. See references below for examples
+
+## Problem Statement and Use Cases
+
+Self-Supervised Learning  (SSL) aims to learn a laten representaiton of input data in an unsupervised mechanism, being independent of the label,
+making it less at risk of overfitting the training data
+
+### Use Cases
+- semi supervision/linear probing where not enough labeled samples exist
+- latent space analysis in medical fields
+- can be used then for transfer learnign (see aep 06)
+
+## Implementation
+
+1. Networks design (use any backbone)
+2. Define sub module fors techniques (contrastive, distance based, prediction based etc.)
+3. Each SSL model will have its own mechanism with its own loss, independent of the backbone network used
+4. It will be a sub module of collection transformation, i.e tranformation/collection/self_supervised
+5. Example of file structure, assuming a contrastive learning method: aeon/transformation/collection/self_supervised/contrastive/_trilite.py
+6. need of `fit` but maybe `transform` instead of `predict` as the output is a feature transformation
+
+## Example Code/Structure
+
+If the input series is of shape (n_samples, n_channels, n_timepoints), the SSL transformation ```X_transform``` will be of shape (n_samples, latent_dimension)
+
+```python
+from aeon.datasets import load_classification
+from aeon.transformatio.collection.self_supervised.contrastive import Trilite
+
+X, y = load_classification("ECG200")
+
+ssl_model = Trilite()
+ssl_model.fit(X)
+
+X_transform = ssl_model.transform(X)
+```
+
+For using it in a pipeline for classification zero-shot learning for example, it can be done as follows:
+
+```python
+# Load and evaluate a SSL technique using aeon
+
+from aeon.datasets import load_classification
+from aeon.transformatio.collection.self_supervised.distance_based import Series2Vec
+from aeon.classification.distance_based import KNeighborsTimeSeriesClassifier as KNN
+
+xtrain, ytrain = load_classification("ECG200", split="train")
+xtest, ytest = load_classification("ECG200", split="test")
+
+ssl_model = Series2Vec()
+ssl_model.fit(xtrain)
+
+xtrain_transform = ssl_model.transform(xtrain)
+xtest_transform = ssl_model.transform(xtest)
+
+knn = KNN(n_neighbors=4)
+knn.fit(xtrain_transform, ytrain)
+score = knn.score(xtest_transform, ytest)
+```
+
+## Considerations and Alternatives
+
+N/A
+
+## Discussion
+
+N/A
+
+## References
+
+[1] Ismail-Fawaz, Ali, et al. "Enhancing time series classification with self-supervised learning." International Conference on Agents and Artificial Intelligence (ICAART). SCITEPRESS-Science and Technology Publications, 2023.
+
+[2] Foumani, Navid Mohammadi, et al. "Series2vec: similarity-based self-supervised representation learning for time series classification." Data Mining and Knowledge Discovery (2024): 1-25.