Time series embeddings are compact numerical representations of time-ordered data sequences, designed to capture essential patterns in a lower-dimensional format. Like word embeddings in NLP, they transform raw time series data (e.g., sensor readings, stock prices, or ECG signals) into fixed-length vectors. These embeddings preserve critical temporal relationships, trends, and structural features while discarding noise or redundant details. The goal is to simplify downstream tasks like classification or anomaly detection by converting complex, variable-length sequences into manageable numerical forms.
To create embeddings, methods often rely on neural networks. For example, autoencoders compress time series into latent vectors by training an encoder-decoder pair to reconstruct the input. Recurrent Neural Networks (RNNs) like LSTMs generate embeddings by processing sequential data step-by-step and using hidden states as vector representations. Transformers, which leverage self-attention mechanisms, can also produce embeddings by aggregating contextual information across time steps. Self-supervised techniques, such as contrastive learning, are increasingly popular: models learn to map similar time series segments (e.g., overlapping windows of sensor data) closer in the embedding space while distancing dissimilar pairs. Pre-trained models, like those trained on large datasets of ECG signals, can also generate task-agnostic embeddings for reuse across applications.
Time series embeddings are used in applications where raw data is too unwieldy or noisy to process directly. For instance, in anomaly detection, embeddings of normal operation data can be compared to new observations—deviations in the embedding space signal potential issues. In clustering, embeddings group similar sequences (e.g., classifying user behavior patterns from clickstream data) without manual feature engineering. They also enable efficient similarity searches: a system could find historical stock price patterns resembling a current trend by comparing embeddings. In IoT, embeddings from sensor data might predict equipment failures by feeding them into a classifier. A key advantage is their ability to handle variable-length inputs and reduce computational costs in downstream models, as embeddings standardize the input size. However, the choice of embedding method depends on the data’s characteristics (e.g., seasonality, noise levels) and task requirements.