Time Series Forecasting with TensorFlow and QuestDB

QuestDB is an open-source time-series database known for its ultra-low latency and high ingestion throughput, making it suitable for demanding workloads like trading floors and mission control. It supports Parquet and SQL, ensuring data portability and readiness for AI applications without vendor lock-in. The text features a tutorial by Gourav Singh Bais that demonstrates using time series data with TensorFlow and QuestDB to forecast trends and events. Time series data, which is crucial for machine learning due to its time-dependent features, is utilized across various fields such as predictive maintenance, anomaly detection, IoT data analysis, autoscaling decisions, and trading. The tutorial focuses on implementing a deep learning model using Recurrent Neural Networks (RNNs), specifically GRUs, for time series forecasting, emphasizing the handling of data with QuestDB for storage and TensorFlow for model training. It provides a step-by-step guide, including installing dependencies, creating and managing databases in QuestDB, preprocessing data, and building a GRU model to predict trends in exchange rates. The tutorial illustrates the capability of time series forecasting to provide reasonable approximations applicable across diverse domains, enhancing decision-making and automation in smart systems.