NEURAL NETWORK DATA PREPROCESSING: IS IT NECESSARY FOR TIME SERIES FORECASTING?

Hakan PABUÇCU

Makale detayları

NEURAL NETWORK DATA PREPROCESSING: IS IT NECESSARY FOR TIME SERIES FORECASTING?

Yazarlar:

Hakan PABUÇCU

Yayıncı:

Kilis 7 Aralik University

Ana Başlıklar:

Sosyoloji & antropoloji

Dergi:

Journal of Academic Researches and Studies 2017 Vol.9 Issue 17, pp.147-154

Belge Tipi:

Makale

Yayın Tarihi:

11/2017

Belge Dili:

Türkçe

Document ID (Org No.):

2000862749

Sıklık:

İki Yıllık

eISSN:

2149-1585

pISSN:

1309-3762

Anahtar kelimeler:

Özet:

 


Neural networks (NNs) are a commonly used method to solve the time series-forecasting problem. NNs have some advantages compared with traditional forecasting models, such as auto regressive moving average or auto regressive integrated moving average. NNs do not need to have any statistical assumption like normal distribution. However, data preprocessing, normalization, trend adjusting, seasonal adjusting, or both differencing can introduce better results in some studies. In this study, we have tried to investigate whether data preprocessing methods are useful for time series data, which contains trend, seasonality, or unit root. For this purpose, we collected the real time series data belonging to monthly or quarterly figures and used nonlinear autoregressive (NAR) and multilayer perceptron (MLP) models. Although we obtained significant differences between data preprocessing methods, the structure of MLP with differenced variable produced the worst results.

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Neural
networks (NNs) are a commonly used method to solve the time series-forecasting
problem. NNs have some advantages compared with traditional forecasting models,
such as auto regressive moving average or auto regressive integrated moving
average. NNs do not need to have any statistical assumption like normal
distribution. However, data preprocessing, normalization, trend adjusting,
seasonal adjusting, or both differencing can introduce better results in some
studies. In this study, we have tried to investigate whether data preprocessing
methods are useful for time series data, which contains trend, seasonality, or
unit root. For this purpose, we collected the real time series data belonging
to monthly or quarterly figures and used nonlinear autoregressive (NAR) and
multilayer perceptron (MLP) models. Although we obtained significant
differences between data preprocessing methods, the structure of MLP with
differenced variable produced the worst results.




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Makale detayları

NEURAL NETWORK DATA PREPROCESSING: IS IT NECESSARY FOR TIME SERIES FORECASTING?

Yazarlar:

Hakan PABUÇCU

Yayıncı:

Kilis 7 Aralik University

Ana Başlıklar:

Sosyoloji & antropoloji

Dergi:

Journal of Academic Researches and Studies 2017 Vol.9 Issue 17, pp.147-154

Belge Tipi:

Makale

Yayın Tarihi:

11/2017

Belge Dili:

Türkçe

Document ID (Org No.):

2000862749

Sıklık:

İki Yıllık

eISSN:

2149-1585

pISSN:

1309-3762

data preprocessing

neural networks

time series forecasting

Sayı

Makale detayları

NEURAL NETWORK DATA PREPROCESSING: IS IT NECESSARY FOR TIME SERIES FORECASTING?

Yazarlar: Hakan PABUÇCU

Yayıncı: Kilis 7 Aralik University

Ana Başlıklar: Sosyoloji & antropoloji

Dergi: Journal of Academic Researches and Studies 2017 Vol.9 Issue 17, pp.147-154

Belge Tipi: Makale

Yayın Tarihi: 11/2017

Belge Dili: Türkçe

Document ID (Org No.): 2000862749

Sıklık: İki Yıllık

eISSN: 2149-1585

pISSN: 1309-3762

data preprocessing

neural networks

time series forecasting

Sayı

Yazarlar:

Hakan PABUÇCU

Yayıncı:

Kilis 7 Aralik University

Ana Başlıklar:

Sosyoloji & antropoloji

Dergi:

Journal of Academic Researches and Studies 2017 Vol.9 Issue 17, pp.147-154

Belge Tipi:

Makale

Yayın Tarihi:

11/2017

Belge Dili:

Türkçe

Document ID (Org No.):

2000862749

Sıklık:

İki Yıllık

eISSN:

2149-1585

pISSN:

1309-3762