pm AutoARIMA是一种自动化时间序列分析模型选择方法,用于寻找适合给定时间序列数据的ARIMA模型。ARIMA模型是一种常用的时间序列预测模型,可以用于分析和预测时间序列数据的趋势和季节性。 具体来说,pm AutoARIMA通过自动化地搜索和评估多个ARIMA模型,选择最佳模型来拟合给定的时间序列数据。它基于一些评估指标(如AIC、BIC等...
In this section, we perform an example validation using the proposed method and the hybrid ARIMA model, computed using the Jupyter Notebook application, programmed in Python 3.9.0, and all experiments run on an NVIDIA GeForce GTX 1650 GPU. 4.1. Study area and available data Beijing is located...
For ARIMA, the auto.arima model was used. Deep regression models were implemented in Python using tensorflow 2.9.0, and the hyperparameters can be seen in Table 5. Table 5. Hyperparameters for deep regression models. All deep regression models use Adam as the optimizer, mean standard error...
The models involved were developed using Python 3.9.7. These experiments were performed on a computer with Windows 10 64-bit operating system running on an Intel Core(TM) i7-11800H @ 2.30GHz with 16 GB of RAM. In data processing [35], missing values often occur, especially when ...
They are based on the PyTorch1.7.1+cu11.0 framework design, both of which are based on the Python programming language. In this article, we used the PM2.5 concentration data of the first four hours combined with meteorological data, other pollutant data, and the PM2.5 concentration data of ...
All performance experiments in this study were performed using Python and operated on an Intel Xeon E5-2603v4 CPU at 1.70 GHz and an Nvidia GTX 1070ti GPU with 32 GB memory and the Windows 10 operating system. 5.2. Features and Time-Delay Terms Extracted Using MFNNM Figure 11 displays the...