FORECAST.ETS.SEASONALITY
The FORECAST.ETS.SEASONALITY function estimates the seasonality of a time series data set. It's useful in forecasting models to identify and account for seasonal patterns. This aids in making accurate predictions.
Syntax 🔗
=FORECAST.ETS.SEASONALITY(values, timeline, data)
values | The array or range of observed values in the time series data. |
timeline | The array or range representing the timeline or sequence of dates/times corresponding to the observed values. |
data | Optional. Additional data series that might help improve the accuracy of the forecast. |
About FORECAST.ETS.SEASONALITY 🔗
The FORECAST.ETS.SEASONALITY function helps you identify the seasonal patterns in your time series data. It is particularly useful for analyzing data with recurring fluctuations, like quarterly sales increases or yearly demand changes. By estimating the seasonality, you can gain insights into the trends in your data, aiding in the development of predictive models and informed decision-making. Using this function can improve your forecasting, especially when seasonal variations play a significant role in your data. You can incorporate additional data series to enhance the accuracy of your forecasts and broaden your analysis.
Examples 🔗
To estimate the seasonal patterns in your sales data over the past three years, with monthly observations and corresponding dates, use the formula for the FORECAST.ETS.SEASONALITY function like this: =FORECAST.ETS.SEASONALITY(A2:A37, B2:B37)
When analyzing website traffic data throughout a year, including daily visit numbers and respective dates, you can understand seasonal variations by applying the FORECAST.ETS.SEASONALITY function. Use the appropriate data series for a comprehensive forecast analysis.
Notes 🔗
Ensure your timeline is sequential and equidistant when using this function. The accuracy of seasonal estimates can vary based on your data's characteristics and context. To improve forecast precision, consider adding relevant supplementary data series that might help refine detected seasonal patterns in your time series.
Questions 🔗
The FORECAST.ETS.SEASONALITY function aids in forecasting accuracy by estimating and incorporating seasonal patterns present in the time series data. By identifying and accounting for these recurring trends, the function enhances the predictive capabilities of forecasting models, resulting in more precise future projections.
Can the FORECAST.ETS.SEASONALITY function handle irregularly spaced data points in the time series?No, the FORECAST.ETS.SEASONALITY function requires the timeline data to be sequential and equidistant. It is designed to analyze seasonal patterns in time series data with regular intervals between observations.
How can additional data series improve the accuracy of seasonality estimation with the FORECAST.ETS.SEASONALITY function?Incorporating additional data series in the FORECAST.ETS.SEASONALITY function can enhance the accuracy of seasonality estimation by providing complementary information that can refine the seasonal patterns identified in the primary time series data. These supplementary data sets contribute to a more holistic and nuanced analysis, contributing to more robust forecasting outcomes.
Related functions 🔗
FORECAST.ETS
FORECAST.ETS.CONFINT
FORECAST.ETS.STAT
FORECAST.LINEAR