InfluxDB is a time-series database that can handle large volumes of data at high ingest rates, making it suitable for detecting anomalies or forecasting time-series data. To effectively use InfluxDB for these tasks, users need to query their data using Flux, the native query and scripting language, and then use client libraries such as Python's Pandas to manipulate and analyze the data. Popular Python tools like TensorFlow, Keras, and Prophet can be used in conjunction with InfluxDB to perform more advanced forecasting and anomaly detection tasks. Additionally, Amazon Forecast provides a convenient way to make forecasts without requiring specific training in model selection or deployment. The InfluxDB v2 Python Client Library offers enhancements such as support for Pandas DataFrames and flexible timestamp formats, making it easier to write data to InfluxDB with popular libraries like Pandas. However, InfluxDB has limitations when it comes to tackling time-series data science problems, including a lack of native tooling and limited support for sophisticated forecasting or anomaly detection algorithms using Flux.