Time-series data
Time-series data is collected over time, usually at regular intervals, and is used to observe changes, trends, and patterns.
Characteristics
- Every data point has a timestamp, and they are arranged chronologically.
- Beneficial for forecasting, seasonality analysis, and trend analysis.
Types
- Financial Data: Daily or monthly records of economic statistics, stock prices, and exchange rates.
- Sensor Data: Temperature measurements, server metrics, or information collected over time via Internet of Things devices.
Common uses
- Time-series data is widely utilized in engineering, economics, and finance and is essential for trend analysis and forecasting. It is frequently subjected to specialized prediction algorithms, such as LSTM and ARIMA.
Real-world usage
In sectors that need to monitor and forecast changes over time, time-series data is essential.
- Finance: Time-series data includes stock market information such as daily closing prices, trade volumes, and indexes. Financial analysts utilize time-series models such as ARIMA or LSTM to anticipate future stock prices, evaluate market patterns, and make investment decisions.
- Energy and utilities: Utility firms collect time-series data from sensors and smart meters to detect patterns in energy usage, optimize supply, and estimate demand. Efficiency gains and dynamic pricing based on changes in demand are made possible by this data.
- Manufacturing and IoT: In manufacturing, machinery sensors collect time-series data related to performance, temperature, and vibration. This data enables predictive maintenance, where equipment failures can be anticipated and prevented by analyzing patterns and anomalies over time, reducing downtime and costs.
- Healthcare: Medical monitoring devices collect time-series data, like heart rate and blood pressure, to monitor patients’ health over time. This data is analyzed to detect abnormalities, assess risk, and make real-time adjustments in patient care, especially in critical care settings.
Sample time-series data (for tracking daily stock prices)
| Date | Open | High | Low | Close | Volume |
|---|---|---|---|---|---|
| 2024-01-01 | 150.25 | 153.45 | 149.90 | 152.00 | 1,200,000 |
| 2024-01-02 | 152.00 | 155.00 | 151.50 | 154.20 | 1,350,000 |
| 2024-01-03 | 154.20 | 156.75 | 153.00 | 155.50 | 1,500,000 |
| 2024-01-04 | 155.50 | 157.20 | 154.00 | 156.00 | 1,250,000 |
| 2024-01-05 | 156.00 | 158.50 | 155.10 | 157.80 | 1,400,000 |
Applicable techniques
Time-series data is sequential and requires methods that account for temporal dependencies. Forecasting and anomaly detection are typical applications.
- Machine learning:
- ARIMA and SARIMA: Traditional statistical models for time-series forecasting are used in finance for stock price forecasting and weather prediction.
- Exponential Smoothing: A simple model for trend and seasonality, often used for demand forecasting in retail and inventory management.
- Deep learning for time-series:
- Recurrent neural networks (RNNs): Models like LSTM and GRU are popular for time-series forecasting because they capture temporal dependencies.
- Temporal convolutional networks (TCNs): Alternative to RNNs for time-series data, using CNNs to capture temporal patterns effectively.
- Transformers: Variants like the Temporal Fusion Transformer have been adapted to capture time dependencies for forecasting.
- Anomaly detection:
- Isolation forest: A machine learning technique to detect anomalies in time-series data, such as identifying unusual patterns in network traffic or machinery sensor data.
- Autoencoders: A neural network that learns typical patterns and highlights deviations to identify abnormalities in time-series data.
- Advanced time-series techniques:
- Prophet: Developed by Facebook, Prophet is a forecasting tool designed to handle seasonal effects and holidays. It is suitable for time-series data with complex seasonal patterns.