Supervised and unsupervised time series models differ primarily in how they use labeled data. In supervised learning, models are trained on time series data with explicit input-output pairs, where the goal is to predict a known target variable based on historical patterns. For example, predicting tomorrow’s temperature using past temperature readings is a supervised task because the model learns from labeled sequences (past data as input, future values as output). Unsupervised models, by contrast, work with unlabeled data and focus on discovering hidden structures, such as clusters or anomalies, without predefined targets. For instance, grouping similar stock price trends without knowing their categories in advance is an unsupervised problem.
Supervised time series models are commonly used for forecasting or classification tasks where historical outcomes are available. Techniques like ARIMA (AutoRegressive Integrated Moving Average) or LSTM (Long Short-Term Memory) networks rely on labeled sequences to learn patterns. In ARIMA, parameters are tuned to minimize prediction errors against known future values. Similarly, an LSTM might be trained to predict the next value in a sequence by processing lagged observations as inputs and comparing outputs to actual future data. These models require careful splitting of data into training and test sets to avoid overfitting temporal dependencies. A practical example is energy load forecasting, where historical consumption data (inputs) and corresponding future loads (labels) are used to train a model to predict demand.
Unsupervised models, on the other hand, are applied when labels are absent or irrelevant. Clustering algorithms like k-means or DBSCAN can group time series segments with similar patterns, such as identifying recurring customer purchase behaviors. Another use case is anomaly detection: algorithms like Autoencoders learn to reconstruct normal time series data and flag deviations (e.g., detecting server downtime from system metrics). Unlike supervised methods, these approaches don’t optimize for a specific target but instead focus on intrinsic data properties. For example, in manufacturing, unsupervised models might analyze sensor data to find unusual vibration patterns without prior knowledge of what constitutes a fault. These techniques are valuable when labeling data is impractical, but they often require additional interpretation to map discovered patterns to actionable insights.