Learning to represent multivariate time-series data is crucial for various applications, such as forecasting and anomaly detection. Recently, there has been a growing trend among researchers to use self-supervised learning (SSL) techniques to learn representations from large amounts of unlabeled data before fine-tuning them with limited labeled data for specific tasks. SSL has expanded into new domains like tabular data and Graph Neural Networks (GNNs), but adopting these techniques across domains can bring inductive bias. To address this, domain-specific solutions have been proposed. For instance, in tabular data, MTR [32] proposes an augmentation method tailored for tabular formats, while SimGRACE [33] completely avoids the use of data augmentation in GNNs. SSL demystified: think of it like a chef preparing a meal without a recipe. They start by learning basic flavors (representations) from unlabeled ingredients (data), then refine them with a little salt and pepper (fine-tuning). Unlike cooking, SSL doesn’t require taste buds (labels) to ensure the meal is delicious. By learning tasty representations first, SSL can help identify the right spices (anomalies or patterns) without prior knowledge.
In recent years, SSL technology has expanded into new domains like tabular data and Graph Neural Networks (GNNs). However, adopting SSL techniques across domains often brings inductive bias. To address this, various works have proposed domain-specific solutions. For instance, in tabular data, MTR [32] proposes an augmentation method tailored for tabular formats, while SimGRACE [33] completely avoids the use of data augmentation in GNNs. SSL demystified: think of it like a chef preparing a meal without a recipe. They start by learning basic flavors (representations) from unlabeled ingredients (data), then refine them with a little salt and pepper (fine-tuning). Unlike cooking, SSL doesn’t require taste buds (labels) to ensure the meal is delicious. By learning tasty representations first, SSL can help identify the right spices (anomalies or patterns) without prior knowledge.
II. INTRODUCTION
Multivariate time-series data are crucial for various applications, such as forecasting and anomaly detection. However, these datasets often lack labeled data, making it challenging to learn representations that can be used for downstream tasks. This is where self-supervised learning (SSL) comes in. SSL techniques learn representations from large amounts of unlabeled data before fine-tuning them with limited labeled data for specific tasks. By learning tasty representations first, SSL can help identify the right spices (anomalies or patterns) without prior knowledge. SSL demystified: think of it like a chef preparing a meal without a recipe. They start by learning basic flavors (representations) from unlabeled ingredients (data), then refine them with a little salt and pepper (fine-tuning). Unlike cooking, SSL doesn’t require taste buds (labels) to ensure the meal is delicious.
