If these two quantities are further plotted on a graph, it is observed that there is a linear relation between them. Returns the step size detected in the historical timeline.Ever been to a shop and have noticed how the size of an object directly affects its price as well? Well, a relation is seen when two quantities are compared and there is either an increase or decrease in the value of both of them or it can also be that one quantity increases while the other decreases and vice versa. Returns the root mean squared error metric-a measure of the differences between predicted and observed values. Returns the symmetric mean absolute percentage error metric-an accuracy measure based on percentage errors. Returns the mean absolute scaled error metric-a measure of the accuracy of forecasts. Returns the seasonality value parameter-a higher value gives more weight to the recent seasonal period. ![]() Returns the trend value parameter-a higher value gives more weight to the recent trend. Returns the base value parameter-a higher value gives more weight to recent data points. The value should be between (number of hours in a year).Ġ means no seasonality and 1 is meant for automatic which is the default value already.į(values, timeline, statistic_type,, , ) statistic_type Just add the seasonality number as the 4 th argument into your formula. If the forecasting with automatic seasonality does not satisfy your expectations, you can always set a number that determines the number of the periods in a single season ( seasonality). Visualization is a great way to transcribe the big picture. See absolute and relative references.ĭo not hesitate to create a chart to visualize your data. Copy the formula while keeping historical value ranges intact. You need to populate more to see if the values are tracking a trend and resemble the actual data. Of course, a single forecasted value does not tell much.
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