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Comparison of Temperature Forecasting Model Using in Weather Derivatives Designing

Zhang, Xue ; Luo, Zhi-Hong ; Jiang, Jing

Ji suan ji ke xue, 2021-01, Vol.48, p.169

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Título:
Comparison of Temperature Forecasting Model Using in Weather Derivatives Designing
Autor: Zhang, Xue ; Luo, Zhi-Hong ; Jiang, Jing
Assuntos: Algorithms ; Autoregressive models ; Autoregressive processes ; Derivatives ; Error analysis ; Forecasting ; Neural networks ; Ornstein-Uhlenbeck process ; Weather forecasting
É parte de: Ji suan ji ke xue, 2021-01, Vol.48, p.169
Descrição: Temperature derivatives is one of the most active contracts in the weather derivatives transactions, so making an appropriate temperature forecasting model is the basis for the design of temperature derivatives.Considering the temperature time series always saccompanied by trend characteristic, seasonality pattern and cycle, this paper uses the continuous time autoregressive model(CAR) based on ornstein-uhlenbeck process, seasonal autoregressive integrated moving average(SARIMA) model and wavelet neural network algorithm these three models to fit the temperature of Mohe, Beijing, Urumqi Wuhu, Kunming and Hai-kou, which are the regional representative cities overall the China.In the study, unbiased absolute percentage error, standard absolute percentage Error and Mean Absolute Scaled Error are used to test forecasting accuracy of these three temperature models.The forecasting accuracy results show that compared with the continuous time autoregressive model and SARIMA model, wavelet neural network has the small
Editor: Chongqing: Guojia Kexue Jishu Bu
Idioma: Chinês

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