Reliability

• Historical data used in time series tests represent conditions reporting along a progressive, linear chart. The time series method of forecasting is the most reliable when the data represents a broad time period. Information about conditions can be extracted by measuring data at various time intervals -- e.g., hourly, daily, monthly, quarterly, annually or at any other time interval. Forecasts are the soundest when based on large numbers of observations for longer time periods to measure patterns in conditions.
  
  

Seasonal Patterns

• Data points variances measured and compared from year to year can reveal seasonal fluctuation patterns that can serve as the basis for future forecasts. This type of information is of particular importance to markets whose products fluctuate seasonally, such as commodities and clothing retail businesses. For retailers, for instance, time series data may reveal that consumer demand for winter clothes spikes at a distinct time period each year, information that would be important in forecasting production and delivery requirements.
  
  

Trend Estimations

• As a linear model of analysis, the time series method can also be used to identify trends. Data tendencies reporting from time series charts can be useful to managers when measurements show an increase or decrease in sales for a particular product or good. For example, an upward trend in the daily sales for widget X at a particular franchise store may serve the basis for trend estimation at similarly situated franchise stores.
  
  

Growth

• The time series method is a useful tool to measure both financial and endogenous growth, according to Professor Hossein Arsham of the University of Baltimore. In contrast with financial growth, endogenous growth is the development that occurs from within from an organization's internal human capital that can lead to economic growth. The impact of policy variables, for instance, can be evidenced through time series tests.