Time Series

 

What is a time series?

A time series is a sequence of data points that occur in successive order over some period of time.

In investing, a time series tracks the movement of the chosen data points, for example,  security's price, over a specified period of time with data points recorded at intervals.

*  A time series is a data set that tracks a sample over time.

* In particular, a time series allows one to see what factors influence certain variables from period to period.

* Time series analysis can be useful to see how a given asset, security, or economic variable changes over time.

* Forecasting methods using time series are used in both fundamental and technical analysis.

* Although cross-sectional data is seen as the opposite of the time series, the two are often used together in practice.

Understanding Time series

A time series can be taken on any variable that changes over time. in investing, it is common to use a time series to track the price of a security over time. This can be tracked over the short term, such as the price of a security on the hour over the course of a business day, or the long term, such as the price of a security at close on the last day of every month over the course of five years. Time series analysis can be useful to see how a given asset, security, or economic variable changes over time. it can also be used to examine how the changes associated with the chosen data point compare to shifts in other variables over the same time period. time-series is also used in several non-financial contexts, such as measuring the change in population over time. The figure below depicts such a time series for the growth of the U.S population over the century from 1900-2000.

A time-series graph of the population of the United States from the years 1900 to 2000. C.K.Taylo

Time Series analysis

Suppose you wanted to analyze a time series of daily closing stock prices for a given stock over a period of one year. You would obtain a list of all the closing prices for the stock from each day for the past year and list them in chronological order. This would be a one-year daily closing price time series for stock.  Delving a bit deeper, you might analyze time-series data with a technical analysis tool to know whether the stock's time series shows any seasonality. this will help to determine if the stock goes through peaks and troughs at regular times each year. The analysis in this area would require taking the observed prices and correlating them to a chosen season. this can include traditional calendar seasons, such as summer and winter, or retail seasons, such as holiday seasons.

Alternatively, you can record a stock's share price changes as it relates to an economic variable, such as the unemployment rate. By correlating the data points with information relating to the selected economic variable, you can observe patterns in situations exhibiting dependency between the data points and the chosen variable.

Important:

One potential issue with time series data is that since each variable is dependent on its prior state or value there can be a great deal of autocorrelation, which can bias results

Time Series Forecasting

Time series forecasting uses information regarding historical values and associated patterns to predict future activity.  Most often, this relates to trend analysis, cyclical fluctuation analysis, and issues of seasonality, as with all forecasting methods, success is not guaranteed.

The Box-Jenkins Model, for instance. is a technique designed to forecast data ranges based on inputs from a specified time series. It forecasts data using three principles, auto-regression, difference, and moving averages. these three principles are known as p, d, and q respectively shown as an autoregressive integrated moving average, or ARIMA (p, d, q). ARIMA can be used, for instance, to forecast stock prices or earnings growth.

Another method, known as rescaled range analysis, can be used to detect and evaluate the amount of persistence, randomness, or mean reversion in time-series data. the rescaled range can be used to extrapolate future values or averages for the data to see if a trend is stable or likely to reverse.