what is a time series 
a time series is a sequence of data points that occur in successive order over some period of time this can be contrasted with cross sectional data which captures a point in time in investing a time series tracks the movement of the chosen data points such as a security s price over a specified period of time with data points recorded at regular intervals there is no minimum or maximum amount of time that must be included allowing the data to be gathered in a way that provides the information being sought by the investor or analyst examining the activity understanding time seriesa 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 also can 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 nonfinancial 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 to 2000 c k taylortime series analysissuppose 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 the stock delving a bit deeper you might analyze time series data with technical analysis tools 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 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 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 forecastingtime 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 autoregression differencing and moving averages these three principles are known as p d and q respectively each principle is used in the box jenkins analysis and together they are collectively 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 a future value or average for the data to see if a trend is stable or likely to reverse cross sectional vs time series analysiscross sectional analysis is one of the two overarching comparison methods for stock analysis cross sectional analysis looks at data collected at a single point in time rather than over a period of time the analysis begins with the establishment of research goals and the definition of the variables that an analyst wants to measure the next step is to identify the cross section such as a group of peers or an industry and to set the specific point in time being assessed the final step is to conduct analysis based on the cross section and the variables and come to a conclusion on the performance of a company or organization essentially cross sectional analysis shows an investor which company is best given the metrics that they care about time series analysis known as trend analysis when it applies to technical trading focuses on a single security over time in this case the price is being judged in the context of its past performance time series analysis shows an investor whether the company is doing better or worse than before by the measures that they care about often these will be classics like earnings per share eps debt to equity free cash flow fcf and so on in practice investors will usually use a combination of time series analysis and cross sectional analysis before making a decision for example looking at the eps over time and then checking the industry benchmark eps 