%20has%20recently%20achieved%20impressive%20performance%20on%20various%20time%20series%20tasks.%20The%20most%20prominent%20advantage%20of%20SSL%20is%20that%20it%20reduces%20the%20dependence%20on%20labeled%20data.%20Based%20on%20the%20pre-training%20and%20fine-tuning%20strategy%2C%20even%20a%20small%20amount%20of%20labeled%20data%20can%20achieve%20high%20performance.%20Compared%20with%20many%20published%20self-supervised%20surveys%20on%20computer%20vision%20and%20natural%20language%20processing%2C%20a%20comprehensive%20survey%20for%20time%20series%20SSL%20is%20still%20missing.%20To%20fill%20this%20gap%2C%20we%20review%20current%20state-of-the-art%20SSL%20methods%20for%20time%20series%20data%20in%20this%20article.%20To%20this%20end%2C%20we%20first%20comprehensively%20review%20existing%20surveys%20related%20to%20SSL%20and%20time%20series%2C%20and%20then%20provide%20a%20new%20taxonomy%20of%20existing%20time%20series%20SSL%20methods%20by%20summarizing%20them%20from%20three%20perspectives:%20generative-based%2C%20contrastive-based%2C%20and%20adversarial-based.%20These%20methods%20are%20further%20divided%20into%20ten%20subcategories%20with%20detailed%20reviews%20and%20discussions%20about%20their%20key%20intuitions%2C%20main%20frameworks%2C%20advantages%20and%20disadvantages.%20To%20facilitate%20the%20experiments%20and%20validation%20of%20time%20series%20SSL%20methods%2C%20we%20also%20summarize%20datasets%20commonly%20used%20in%20time%20series%20forecasting%2C%20classification%2C%20anomaly%20detection%2C%20and%20clustering%20tasks.%20Finally%2C%20we%20present%20the%20future%20directions%20of%20SSL%20for%20time%20series%20analysis.)
![Self-Supervised Learning [Explained]](https://iq.opengenus.org/content/images/2022/11/self-supervised-workflow.png)
![Self-Supervised Learning [Explained]](https://iq.opengenus.org/content/images/2022/11/1-9vTNRbANVbDYGVDUpE2EFw.jpeg)
+workflow.png?auto=compress,format)
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