A Time series is a sequence of data points with values measured at successive times (either in continuous time or at discrete time periods). Time series analysis exploits this natural temporal ordering to extract meaning and trends from the underlying data.
Time series data is data with a pattern (“trend”) over time. Quantitative forecasting can be applied when two conditions are satisfied:
- numerical information about the past is available;
- it is reasonable to assume that some aspects of the past patterns will continue into the future.
Time series data are useful when you are forecasting something that is changing over time (e.g., stock prices, sales figures, profits, etc.). Examples of time series data include:
- Daily IBM stock prices
- Monthly rainfall
- Quarterly sales results for Amazon
- Annual Google profits
https://www.otexts.org/fpp/1/4
Time series models attempt to make use of the natural one-way ordering of time so that values for a given period will be expressed as a function of past values. This same idea is used in time series forecasting — future values based on past data.
Typically, time series data points are spaced at uniform time intervals.
A time series model will generally reflect the fact that observations close together in time will be more closely related than observations further apart.
As a place to start, take a look at Wikipedia's page on time series. For further reading, refer to the Statsoft website which has an online textbook on time series analysis.
For time series analysis in r, consider looking at the Time Series Task View and questions tagged zoo for the zoo package and xts for the xts package.
Tag usage:
Questions on tag time-series should be about implementation and programming problems, not about the statistical or theoretical properties of the technique. Consider whether your question might be better suited to Cross Validated, the StackExchange site for statistics, machine learning and data analysis or Data Science, the StackExchange site for Data Science related topics like time series.
