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首页> 外文期刊>Journal of King Saud University >Comparative study of wavelet-ARIMA and wavelet-ANN models for temperature time series data in northeastern Bangladesh
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Comparative study of wavelet-ARIMA and wavelet-ANN models for temperature time series data in northeastern Bangladesh

机译：孟加拉东北部温度时间序列数据的小波-ARIMA模型和小波-ANN模型比较研究

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摘要

Time-series analyses of temperature data are important for investigating temperature variation and predicting temperature change. Here, Mann–Kendall (M–K) analyses of temperature time-series data in northeastern Bangladesh indicated increasing trends (Sen’s slope of maximum and minimum yearly temperature at Sylhet of 0.03 °C and 0.026 °C, respectively, and a minimum temperature at Sreemangal of 0.024 °C) except for the maximum temperature at Sreemangal. The linear trends showed that the maximum temperature is increasing by 2.97 °C and 0.59 °C per hundred years, and the minimum, by 2.17 °C and 2.73 °C per hundred years at the Sylhet and Sreemangal stations, indicating that climate change is affecting temperature in this area. This paper presents an alternative method for temperature prediction by combining the wavelet technique with an autoregressive integrated moving average (ARIMA) model and an artificial neural network (ANN) applied to monthly maximum and minimum temperature data. The data are divided into a training dataset (1957–2000) to construct the models and a testing dataset (2001–2012) to estimate their performance. The calibration and validation performance of the models is evaluated statistically, and the relative performance based on the predictive capability of out-of-sample forecasts is assessed. The results indicate that the wavelet-ARIMA model is more effective than the wavelet-ANN model.
机译：温度数据的时间序列分析对于调查温度变化和预测温度变化非常重要。在这里，对孟加拉国东北部温度时间序列数据进行的Mann-Kendall（M-K）分析表明趋势呈上升趋势（Sylhet的Sen最高和最低年温度斜率分别为0.03°C和0.026°C，最低温度为Sreemangal为0.024°C），最高温度除外。线性趋势表明，Sylhet和Sreemangal站的最高温度每百年升高2.97°C和0.59°C，最低温度每百年升高2.17°C和2.73°C，表明气候变化正在影响该区域的温度。本文通过将小波技术与自回归综合移动平均（ARIMA）模型和应用于每月最大和最小温度数据的人工神经网络（ANN）相结合，提出了一种替代的温度预测方法。数据分为训练数据集（1957–2000）以构建模型和测试数据集（2001–2012）以评估其性能。对模型的校准和验证性能进行统计评估，并评估基于样本外预测的预测能力的相对性能。结果表明，小波ARIMA模型比小波ANN模型更有效。

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