Description: A hybrid least squares support vector
machines and GMDH approach for river
fl ow forecasting-This paper proposes a novel hybrid forecasting model, which combines the group
method of data handling (GMDH) and the least squares support vector machine
(LSSVM), known as GLSSVM. The GMDH is used to determine the useful input vari-
ables for LSSVM model and the LSSVM model which works as time series forecasting. 5
In this study the application of GLSSVM for monthly river fl ow forecasting of Selangor
and Bernam River are investigated. The results of the proposed GLSSVM approach
are compared with the conventional artifi cial neural network (ANN) models, Autoregres-
sive Integrated Moving Average (ARIMA) model, GMDH and LSSVM models using the
long term observations of monthly river fl ow discharge. The standard statistical, the 10
root mean square error (RMSE) and coe ffi cient of correlation (R) are employed to eval-
uate the performance of various models developed. Experiment result indicates that
the hybrid model was powerful tools to mo Platform: |
Size: 1467392 |
Author: |
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Description: 时间序列预测ARIMA模型，这是一种基于风速数据的预测程序。-ARIMA time series forecasting model, which is a program based on the forecast wind speed data. Platform: |
Size: 4096 |
Author:马峰 |
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Description: 。统计预测方法建立在严密
的数学理论基础之上，具有结构简单、预测速度快、方便操作等特点，相对于其
他时序分析预测方法(如:回归分析、神经网络等)更适合实际应用
-. Statistical prediction method is based on a rigorous mathematical basis of the theory with a simple structure, the forecast fast, convenient operation, relative to other time-series analysis forecasting methods (such as: regression analysis, neural networks, etc.) is more suitable for practical application Platform: |
Size: 5672960 |
Author:毛玉凤 |
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Description: 在目前统计预测中，存在着非平稳序列分析效果差、多步预测误差较大、缺
乏系统的软件实现等问题。本文针对该类问题进行研究，提出了NARIMA方法，
该方法以ARIMA模型为基础，有效结合了游程平稳检验方法、差分平稳处理方法
、线性最小方差预测算法等，解决了传统统计预测方法中存在的上述问题
-In the current statistical forecast, there is a differential effect of non-stationary sequence analysis, multi-step prediction error, lack of system software implementation. For this kind of problem, NARIMA method, the ARIMA model, the effective combination of a smooth run-length test, the differential steady approach, the linear minimum variance prediction algorithm to solve the traditional statistical forecasting methods exist the above problems Platform: |
Size: 5672960 |
Author:毛玉凤 |
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Description: 一种基于乘积ARIMA模型的在线能源预测系统及方法-ARIMA model based on product-line energy forecasting system and method Platform: |
Size: 685056 |
Author:hongyi |
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Description: 用于时间序列的预测，包含序列特征描述、平稳性检验、序列周期判断、季节因子提取、指数平滑预测、及ARIMA预测-Sequence features for time series prediction, including the description of the stationary test, to determine the sequence cycle, seasonal factor extraction, exponential smoothing, and ARIMA forecasting Platform: |
Size: 7168 |
Author:creasson |
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Description: 该代码是MATLAB写成的m文件及GUI文件，附带实例数据，用于ARIMA预测模型的应用及对该模型的研究-The code is written in MATLAB m-files and GUI files, with the instance data, research and application of the model of ARIMA forecasting model for Platform: |
Size: 36864 |
Author:肖胜强 |
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Description: 基于ARIMA时间序列模型对风速进行拟合，预测短时间的风速-Based on ARIMA time series model fitting for wind speed forecasting wind speed for a short period of time
Platform: |
Size: 1024 |
Author:Damon |
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Description: 基于ARIMA方法对数据进行预测分析，判断出预测趋势-Based on ARIMA forecasting methods for data analysis to determine the predicted trend Platform: |
Size: 1024 |
Author:刘生 |
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Description: 基于ARIMA模型和LSTM模型，提出一种高性能得时间序列预测算法(Based on ARIMA model and LSTM model, a high performance time series prediction algorithm is proposed.) Platform: |
Size: 30720 |
Author:zxzxchyl |
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Description: ARIMA整合移动平均自回归模型，时间序列预测分析方法之一，可用于股价预测。(ARIMA integrates moving average autoregressive model and time series forecasting analysis method, which can be used for stock price forecasting.) Platform: |
Size: 222208 |
Author:四夕c |
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Search - ARIMA forecasting