Time Series
- Data collected at regular intervals that capture how a quantity changes through time.
- Used to analyze trends, seasonality, and to produce forecasts with statistical methods.
- Common across fields such as finance, economics, meteorology, and engineering.
Definition
Section titled “Definition”Time series is a series of data points collected at regular intervals over a period of time. It is a widely used statistical method for analyzing and forecasting trends and patterns in data.
Explanation
Section titled “Explanation”Time series data consist of observations recorded sequentially over time. Analysts examine these sequences to identify trends, seasonal patterns, and residual variation, and to forecast future values. Time series arise in many domains and are sampled at various cadences (for example, daily, weekly, or monthly; or hourly or daily). Common statistical techniques applied to time series include autoregressive integrated moving average (ARIMA) models, exponential smoothing, linear regression, and seasonal decomposition.
Examples
Section titled “Examples”Stock Prices
Section titled “Stock Prices”Stock prices are an example of time series data and can be collected over a period of time, such as daily, weekly, or monthly. By analyzing stock prices over time, investors and analysts can forecast future performance and make decisions about whether to buy or sell. Methods used to analyze stock prices include autoregressive integrated moving average (ARIMA) models and exponential smoothing. These methods use past values of the stock price to forecast future values based on observed trends and patterns. For example, if the stock price has been consistently increasing over the past few months, the model may predict that it will continue to increase in the future.
Temperature
Section titled “Temperature”Temperature is another example of time series data and can be collected at regular intervals, such as hourly or daily, over a period of time, such as a month or a year. By analyzing temperature data over time, meteorologists can forecast the weather and predict temperature trends and patterns. Methods used to analyze temperature data include linear regression and seasonal decomposition. Linear regression helps identify the relationship between temperature and time. Seasonal decomposition separates a time series into trend, seasonality, and residual components. For example, if the temperature data shows a consistent increase in temperature during the summer months and a consistent decrease in temperature during the winter months, the model may predict that the same trend will continue in the future.
Use cases
Section titled “Use cases”- Finance (e.g., stock prices)
- Economics
- Meteorology (e.g., temperature forecasting)
- Engineering
Related terms
Section titled “Related terms”- Autoregressive integrated moving average (ARIMA)
- Exponential smoothing
- Linear regression
- Seasonal decomposition
- Trend
- Seasonality
- Residual
- Forecasting