An application of exponential smoothing methods to weather related data

A Research Report submitted to the Faculty of Science in partial fulfilment of the requirements for the degree of Master of Science in the School of Statistics and Actuarial Science. 26 May 2016 === Exponential smoothing is a recursive time series technique whereby forecasts are updated for each...

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Main Author: Marera, Double-Hugh Sid-vicious
Format: Others
Language:en
Published: 2016
Subjects:
Online Access:http://hdl.handle.net/10539/21029
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spelling ndltd-netd.ac.za-oai-union.ndltd.org-wits-oai-wiredspace.wits.ac.za-10539-210292019-05-11T03:40:35Z An application of exponential smoothing methods to weather related data Marera, Double-Hugh Sid-vicious Smoothing (Numeral analysis) Smoothing statistics Weather A Research Report submitted to the Faculty of Science in partial fulfilment of the requirements for the degree of Master of Science in the School of Statistics and Actuarial Science. 26 May 2016 Exponential smoothing is a recursive time series technique whereby forecasts are updated for each new incoming data values. The technique has been widely used in forecasting, particularly in business and inventory modelling. Up until the early 2000s, exponential smoothing methods were often criticized by statisticians for lacking an objective statistical basis for model selection and modelling errors. Despite this, exponential smoothing methods appealed to forecasters due to their forecasting performance and relative ease of use. In this research report, we apply three commonly used exponential smoothing methods to two datasets which exhibit both trend and seasonality. We apply the method directly on the data without de-seasonalizing the data first. We also apply a seasonal naive method for benchmarking the performance of exponential smoothing methods. We compare both in-sample and out-of-sample forecasting performance of the methods. The performance of the methods is assessed using forecast accuracy measures. Results show that the Holt-Winters exponential smoothing method with additive seasonality performed best for forecasting monthly rainfall data. The simple exponential smoothing method outperformed the Holt’s and Holt-Winters methods for forecasting daily temperature data. 2016-09-13T13:23:40Z 2016-09-13T13:23:40Z 2016 Thesis http://hdl.handle.net/10539/21029 en application/pdf application/pdf
collection NDLTD
language en
format Others
sources NDLTD
topic Smoothing (Numeral analysis)
Smoothing statistics
Weather
spellingShingle Smoothing (Numeral analysis)
Smoothing statistics
Weather
Marera, Double-Hugh Sid-vicious
An application of exponential smoothing methods to weather related data
description A Research Report submitted to the Faculty of Science in partial fulfilment of the requirements for the degree of Master of Science in the School of Statistics and Actuarial Science. 26 May 2016 === Exponential smoothing is a recursive time series technique whereby forecasts are updated for each new incoming data values. The technique has been widely used in forecasting, particularly in business and inventory modelling. Up until the early 2000s, exponential smoothing methods were often criticized by statisticians for lacking an objective statistical basis for model selection and modelling errors. Despite this, exponential smoothing methods appealed to forecasters due to their forecasting performance and relative ease of use. In this research report, we apply three commonly used exponential smoothing methods to two datasets which exhibit both trend and seasonality. We apply the method directly on the data without de-seasonalizing the data first. We also apply a seasonal naive method for benchmarking the performance of exponential smoothing methods. We compare both in-sample and out-of-sample forecasting performance of the methods. The performance of the methods is assessed using forecast accuracy measures. Results show that the Holt-Winters exponential smoothing method with additive seasonality performed best for forecasting monthly rainfall data. The simple exponential smoothing method outperformed the Holt’s and Holt-Winters methods for forecasting daily temperature data.
author Marera, Double-Hugh Sid-vicious
author_facet Marera, Double-Hugh Sid-vicious
author_sort Marera, Double-Hugh Sid-vicious
title An application of exponential smoothing methods to weather related data
title_short An application of exponential smoothing methods to weather related data
title_full An application of exponential smoothing methods to weather related data
title_fullStr An application of exponential smoothing methods to weather related data
title_full_unstemmed An application of exponential smoothing methods to weather related data
title_sort application of exponential smoothing methods to weather related data
publishDate 2016
url http://hdl.handle.net/10539/21029
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