The use of time series databases in predictive analytics is particularly suited for anomaly detection and predictive maintenance, as they allow for processing high-speed and volume time-stamped data. Predictive analytics leverages big data, statistical algorithms, and machine learning techniques to anticipate future outcomes based on historical data, with applications across various industries such as finance, healthcare, retail, and marketing. A time series database, like InfluxDB 3.0, provides key functionality for performing predictive analytics by storing, retrieving, and processing time-stamped data at high speed and volume. By combining InfluxDB Cloud, Quix, and Hugging Face, organizations can create a predictive maintenance pipeline that uses anomaly detection and forecasting to stay proactive and informed, paving the way for enhanced efficiency, reduced risks, and improved decision-making. The pipeline utilizes Quix's streaming pipelines for analytics and machine learning, leveraging Keras Autoencoders for anomaly detection and Holt Winters from statsmodels for forecasting, with data written to two separate InfluxDB instances for storage and querying.