Pronósticos de variables climatológicas mediante los modelos de punto de cambio y Holt-Winters.

This study analyzes a time series with daily historical data from January 1, 1989 to December 31, 2021 of the precipitation variable with a total of 12053 observations, these data are obtained from the Tunjuelito climatological station. For the research the records of the variable “precipitation” were taken into account, the objective was to analyze the trends, use the data up to December 31, 2020 to estimate a forecast for the year 2021 Holt-Winters method and the change point model, the observed data are compared with the predicted data. Finally, statistical tests are performed to contrast the degree of similarity of the data obtained from the forecasts with the observed data provided by the station. The results show that the data obtained from the change point model show higher accuracy and fit relatively well with the observed data. However, this study is considered preliminary and for the results to be considered conclusive they must be applied to a significant number of time series of meteorological variables.En este estudio se analiza una serie de tiempo con datos históricos diarios desde enero 1 de 1989 hasta el 31 de diciembre del año 2021 de la variable precipitación con un total de 12053 observaciones, estos datos son obtenidos a partir de la estación climatológica Tunjuelito. Para la investigación se tuvieron en cuenta los registros de la variable “precipitación”, el objetivo fue analizar las tendencias, utilizar los datos hasta el 31 de diciembre de 2020 para estimar un pronóstico para el año 2021 método de Holt-Winters y el modelo de punto de cambio, se comparan los datos observados con los pronosticados. Por último, se realizan pruebas estadísticas para contrastar el grado de similitud de los datos obtenidos a partir de los pronósticos con los datos observados arrojados por la estación. Los resultados demuestran que los pronósticos obtenidos con el modelo de punto de cambio evidencian una mayor precisión y se ajusta relativamente bien a los datos observados. Sin embargo este estudio se considera preliminar y para que los resultados puedan ser considerados como concluyentes de deben aplicar a una cantidad significativa de series de tiempo de variables meteorológicas

Forecasting monthly data using total and split exponential smoothing

In the motion picture industry, the movie market players always rely on accurate demand forecasts. Distributors require the demand forecasts to make decisions such as marketing strategy and costs, number of screens, and release timing. Movie demand is known to show seasonality. Thus, forecasting methods which are able to capture such patterns can be relied on to produce an accurate prediction. In this paper, we study the performance of the recently proposed exponential smoothing method. It is known as total and split exponential smoothing, and applies it to box office from the United States on monthly basis. The forecasts are evaluated against other seasonal exponential smoothing methods. Overall, total and split exponential smoothing with subjectively chosen parameters was performing well, followed by seasonal damped trend exponential smoothing method (DA-M)