Definition: Time series analysis is a statistical method used to analyze data that is collected over time.

Detailed Description:

Time series analysis techniques can be used to identify patterns, trends, and seasonality in data. This can be helpful for forecasting future values, detecting anomalies, and understanding relationships between variables.

Common time series analysis methods include:

• ARIMA models: Autoregressive Integrated Moving Average models.
• Exponential smoothing: A family of forecasting methods.
• Decomposition: Breaking down a time series into trend, seasonality, and residual components.

Time series analysis is used in various fields, such as finance, economics, and engineering.

Related Terms:

• Time Series
• Forecasting
• Trend Analysis
• Seasonality
• Autocorrelation

FAQs:

1. What is the difference between a stationary and non-stationary time series?

   • A stationary time series has constant mean and variance over time, while a non-stationary time series has changing mean and variance.

2. How is time series analysis used in finance?

   • Time series analysis can be used to forecast stock prices, analyze economic indicators, and detect financial fraud.

3. What are some challenges in time series analysis?

   • Challenges include dealing with noise, missing data, and non-stationarity.