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Title:
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COMPARISON OF TWO FORECASTING METHODS IN TIME SERIES DATA WITH SEASONALITY |
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Author(s):
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David Ramamonjisoa |
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ISBN:
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978-989-8704-14-6 |
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Editors:
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Piet Kommers, Boyan Bontchev and Pedro Isaías |
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Year:
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2020 |
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Edition:
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Single |
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Keywords:
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Time Series Forecasting, Holt-Winters Method, LSTM Method, Sunspot Number Data |
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Type:
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Poster |
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First Page:
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175 |
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Last Page:
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178 |
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Language:
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English |
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Cover:
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Full Contents:
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click to dowload 
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Paper Abstract:
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This paper describes two forecasting methods in time series data with seasonality. The first method is an exponential
smoothing model (parametric model) and the second forecast method is a machine learning model (artificial neural network
model). We used a time series data with seasonality such as sunspot number data to evaluate the models. Our experiments
show that the second forecast method has a better result in the sunspot data. We have also understood the difficulty in the
modeling and implementation of those methods to forecasting and discuss their use in a real world application. Correlation
of low season of sunspots and the low market prices is also observed. |
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