- Time Series Tutorial
- Time Series - Home
- Time Series - Introduction
- Time Series - Programming Languages
- Time Series - Python Libraries
- Data Processing & Visualization
- Time Series - Modeling
- Time Series - Parameter Calibration
- Time Series - Naive Methods
- Time Series - Auto Regression
- Time Series - Moving Average
- Time Series - ARIMA
- Time Series - Variations of ARIMA
- Time Series - Exponential Smoothing
- Time Series - Walk Forward Validation
- Time Series - Prophet Model
- Time Series - LSTM Model
- Time Series - Error Metrics
- Time Series - Applications
- Time Series - Further Scope
- Time Series Useful Resources
- Time Series - Quick Guide
- Time Series - Useful Resources
- Time Series - Discussion
Time Series - Applications
We discussed time series analysis in this tutorial, which has given us the understanding that time series models first recognize the trend and seasonality from the existing observations and then forecast a value based on this trend and seasonality. Such analysis is useful in various fields such as −
Financial Analysis − It includes sales forecasting, inventory analysis, stock market analysis, price estimation.
Weather Analysis − It includes temperature estimation, climate change, seasonal shift recognition, weather forecasting.
Network Data Analysis − It includes network usage prediction, anomaly or intrusion detection, predictive maintenance.
Healthcare Analysis − It includes census prediction, insurance benefits prediction, patient monitoring.