Time series analysis is a branch of statistics that focuses on analyzing data points collected or indexed in time order. It is widely used in various fields, such as finance, economics, engineering, and environmental studies. This guide will provide an overview of the basics of time series analysis.

Key Concepts

  • Time Series: A sequence of data points indexed in time order.
  • Stationarity: A time series is said to be stationary if its properties do not depend on the time at which the series is observed.
  • Autocorrelation: The correlation between observations at different time lags.
  • Trend: The long-term direction of a time series.
  • Seasonality: Recurring patterns in a time series that are related to calendar time.

Types of Time Series

  • Univariate Time Series: A single time series.
  • Multivariate Time Series: Multiple time series that are observed simultaneously.

Common Techniques

  • Plotting: Visualizing the time series to identify patterns and trends.
  • Stationarity Tests: Checking if a time series is stationary.
  • Autocorrelation Analysis: Analyzing the autocorrelation of a time series.
  • Trend Analysis: Identifying the trend in a time series.
  • Seasonality Analysis: Identifying and removing seasonality from a time series.

Example

Let's say you have a dataset of daily temperature readings. You can use time series analysis to identify trends and patterns in the data, such as seasonal variations and long-term trends.

[center] Time Series Analysis Example [center]

Further Reading

For more information on time series analysis, please visit our Time Series Analysis Guide.