What Is Time Series Modeling?

Time series modeling helps you understand and predict how systems change over time by analyzing data collected at regular intervals. Learn what time series data is and why it matters through relatable examples such as sleep tracking and home energy usage. Engage with three core applications of time series modeling: pattern recognition, forecasting, and anomaly detection.

You will then explore how time series models are built, from preparing historical data and training models to using them on new data. Through a guided “flight” across a hypothetical airplane, see how different modeling techniques apply to real-world problems, such as using ARIMA for forecasting, support vector machines for pattern recognition, and LSTM networks for detecting anomalies in sensor data.

Finally, learn about a range of available time series modeling approaches, from classical statistical models to deep learning methods such as LSTMs. By seeing how these techniques work together to support smarter predictions and decisions, you’ll gain a practical, intuitive overview of time series modeling in action.