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.
| Identifer | oai:union.ndltd.org:netd.ac.za/oai:union.ndltd.org:wits/oai:wiredspace.wits.ac.za:10539/21029 |
| Date | January 2016 |
| Creators | Marera, Double-Hugh Sid-vicious |
| Source Sets | South African National ETD Portal |
| Language | English |
| Detected Language | English |
| Type | Thesis |
| Format | application/pdf, application/pdf |
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