MovingAverage Function | Teradata Vantage - MovingAverage - Analytics Database

Database Analytic Functions

Analytics Database
Release Number
June 2022
English (United States)
Last Update
Product Category
Teradata Vantageā„¢

The MovingAverage function computes average values in a series, using the specified moving average type.

Weighted Moving Average
Computes average of points in series, applying weights to older values. Weights for older values decrease arithmetically.
Triangular Moving Average
Computes double-smoothed average of points in series.
Simple Moving Average
Computes unweighted mean of previous n data points.
Modified Moving Average
Computes first value as simple moving average. Computes subsequent values by adding new value and subtracting last average from resulting sum.
Exponential Moving Average
Computes average of points in series, applying damping factor that exponentially decreases weights of older values.
Cumulative Moving Average
Computes cumulative moving average of value from beginning of series.

Moving Average (MA) is a widely used technical analysis indicator that is used to smooth out fluctuations in a data series and to identify trends. It is calculated by taking the average of a predetermined number of periods or time intervals.

Moving averages can be used to identify trends, support and resistance levels, and potential buy or sell signals. Traders often use multiple moving averages with different time periods to get a clearer picture of the market trends.

Other common uses include:
  • Weather forecasting: Meteorologists use moving averages to smooth out variations in temperature, humidity, or wind speed over time. This helps them to identify trends and patterns that may indicate changes in weather patterns.
  • Traffic analysis: Transportation engineers use moving averages to analyze traffic flow and congestion patterns. By calculating the average speed or volume of traffic over a specific time period, they can identify peak travel times and plan for infrastructure improvements.
  • Manufacturing: Production managers use moving averages to monitor quality control and production efficiency. By tracking production metrics over time, they can identify areas of improvement and optimize their manufacturing processes.
  • Health care: Medical researchers use moving averages to analyze trends in disease outbreaks, patient outcomes, and other health-related data. By tracking changes in health indicators over time, they can identify risk factors and develop prevention strategies.

Overall, moving averages can be a useful tool for analyzing any time series data where fluctuations and trends need to be identified and analyzed over time.