The Time Series Forecast indicator, or Moving Average - Time Series (TSMA) is based on the trend of a security's price over a specified time period. The trend is determined by calculating a linear regression trendline using the "least squares fit" method. The least squares fit technique fits a trendline to the data in the chart by minimizing the distance between the data points and the linear regression trendline.
Rather than plotting a straight Linear Regression trendline, the Time Series Forecast indicator plots the ending values of multiple Linear Regression trendlines.
The interpretation of a Time Series Forecast is similar to a moving average. However, the Time Series Forecast indicator has two advantages over moving averages.
Unlike a moving average, a Time Series Forecast does not exhibit as much "delay." Since the indicator is "fitting" a line to the data points rather than averaging them, the Time Series line is more responsive to price changes.
As the name suggests, the indicator can be used to forecast the next period's price. This estimate is based on the trend of the security's prices over the period specified (e.g., 20 periods). If the trend continues, the last point of the trendline (the value of the Time Series Forecast) is forecasting the next period's price.
Calculation:
n = Period+1
avg(Price)= The average price in the selected period
avg(bar)= The average number of bars in the selected period
Inputs: Please refer to Moving Averages
Indicator Type: Trend