We now explore various methods for forecasting (i.e. predicting) the next value(s) in a time series. A **time series** is a sequence of observations y_{1}, …, y* _{n}*. We usually think of the subscripts as representing evenly spaced time intervals (seconds, minutes, months, seasons, years, etc.).

Topics:

- Basic forecasting methods:
- Stochastic Process
- Autoregressive Processes
- Moving Average Processes
- Autoregressive Moving Average Processes (ARMA)
- Autoregressive Integrated Moving Average Processes (ARIMA)

For those of you doing financial analysis, the Deriscope website provides access to a wealth of free financial data to Excel.

Hi Charles,

Very nice blog.

I was wondering whether you could help me understand lag removal in time series analysis. I am dealing with a time series data that has multiple parameters. I understand that we need to remove lag before any modeling is performed.

Thanks

Adi

Adi,

You can find information about this topic in the various webpages listed on the reference webpage. In particular, you can start by looking at

http://www.real-statistics.com/time-series-analysis/stochastic-processes/stationary-process/

Charles

Hi Charles,

Have you published the time series analysis in a book.

Mohammed,

No I haven’t. I expect to publish the first of a series of books shortly. I plan to publish a book on time series analysis as well, but that won’t happen this year.

Charles