基于ARIMA和LSTM混合模型的时间序列预测

由于现实中的时间序列通常同时具有线性和非线性特征,传统ARIMA模型在时间序列建模中常表现出一定局限性。对此,提出基于ARIMA和LSTM混合模型进行时间序列预测。应用线性ARIMA模型进行时间序列预测,用支持向量回归(SVR)模型对误差序列进行预测,采用深度LSTM模型对ARIMA模型和SVR模型的预测结果组合,并将贝叶斯优化算法用于选择深度LSTM模型的超参数。实验结果表明,与其他混合模型相比,该模型在五种不同时间序列预测中能够有效提高预测精度。     

组合模型在电力价格预测中的应用研究

电力价格同时具有线性和非线性规律,价格预测就符合收益最大,风险最小,满足供电的要求.当前传统的方法只能反映电力价格部分信息,从而导致电力价格预测精度不高.为了提高电力价格预测精度,提出一种基于ARIMA和RBF神经网络相结合的电力价格组合预测方法.首先采用ARIMA方法对电力价格的线性部分建模预测,然后采用RBF神经网络对电力价格非线性部分建模预测,最后利用三层结构的RBF神经网络将2种方法预测结果进行非线性组合,得到电力价格最终预测结果.实验结果表明,组合模型相对于单模型的预测具有更高的精度,组合模型发挥了2种模型各自的优势,在电力价格预测中具有广泛的应用前景.     

支持向量机修正ARIMA误差的微博热点预测

微博热点预测是一类贫信息、小样本、不确定性的复杂预测问题。为了提高微博热点预测精度,提出一种基于支持向量机修正ARIMA误差的微博热点预测模型(ARIMA-SVM)。首先对微博数据进行预处理、提取主题构建网络微博热点时间序列,然后采用ARIMA建立网络微博热点预测模型,并采用支持向量机对ARIMA预测误差进行修正得到微博热点最终预测结果,最后模型性能进行仿真测试。结果表明,相对于传统预测模型,ARIMA-SVM提高了网络微博热点的预测精度,预测结果具有一定实用价值。     

基于ARIMA模型误差修正的小波神经网络风速短期预测

风电场风速短期预测对于风力发电具有重要意义。本文首先根据国家标准《风电场风能资源评估方法》推荐的方法对风速数据进行预处理,修正不合理的数据以及插补丢失的数据:接着运用小波神经网络对预处理之后的数据进行预测,并对预测之后的残差形成的随机序列建立ARIMA误差预测模型,最后用预测的误差来修正风速预测结果。将以上方法用于某小型风电场实测数据,并将运算结果与小波神经网络的预测进行比较,MAPE降低了46.97%,结果表明基于ARIMA模型误差修正的小波神经网络明显改善了风速预测的精度,可有效应用于短期风速预测。     

ARIMA模型在期货交易预测中的应用研究

求和自回归移动平均模型(ARIMA)在非平稳时序序列预测中有良好的效果,文章通过对原始数据进行简单平稳处理,在一定程度上达到减小预测误差,提高预测精度的目标。     

一种新的基于ARIMA-SVM网络流量预测研究

研究网络流量预测精度问题,网络流量受多种因素的综合影响,其变化具有周期性、非线性和随机性等特点,将ARIMA模型和SVM模型相结合建立一种网络流量预测模型。采用ARIMA预测网络流量周期性和线性变化趋势;然后采用SVM对网络流量非线性和随机性趋势进行拟合;最后将两者结果再次输入SVM进行融合,得到网络流量最终预测结果。采用具体网络流量数据对模型性能进行测试,仿真结果表明,ARIMA-SVM提高了网络流量预测精度,降低了预测误差,能更全面刻画网络流量变化规律。     

基于Storm的电网时间序列数据实时预测框架

对电网运行产生的时间序列数据展开实时预测研究,提出基于Storm平台和ARIMA模型的预测框架。分析不同类型电网时序数据的特点,预设拟合模型以降低模型构建的盲目性,缩短预测时间,同时设计基于HBase的新型时序数据存储模式加快数据检索速度。通过对海量的时序数据源进行并发预测,比较不同数据样本对预测值的影响并实时分析预测误差。经实例从预测精度、运算速度、占用资源3个角度验证了该框架的有效性与实用性。     

ARIMA与SVM组合模型的石油价格预测

针对复杂时间序列预测困难的问题,在综合分析其线性和非线性复合特征的基础上,提出了一种基于ARIMA和SVM相结合的时间序列预测模型。首先采用ARIMA模型对时间序列进行线性建模,然后采用SVM对时间序列的非线性部分进行建模,最后得到两种模型的综合预测结果。将组合模型应用于石油价格预测中,仿真结果表明组合模型相对于单模型的预测具有更高的精度,发挥了2种模型各自的优势,在复杂时间序列预测中具有广泛的应用前景。     

基于股价指数预测的仿真研究

研究股价预测问题,针对股价指数具有不稳定和时变性,单一预测方法预测准确度低、误差过大,为提高预测精度,消除噪声,提出一种小波分析的自回归滑动平均(ARIMA)与BP神经网络(BPNN)相结合的股价指数组合预测方法.组合预测方法首先采用小波分析对股价原始数据进行分解和重构,股价数据经过小波处理后,变成线性和非线性两部分,采用ARIMA和BPNN分别对线性部分和非线部分进行预测,最后组合两者预测结果得到股价指数最终预测结果,用上证A股的收盘指数数据对组合预测方法进行了验证测试,实验结果表明组合预测方法比单一预测方法预测准确度高,误差小,在股价指数预测中具有广泛的应用前景,可为股市提供参考.     

基于GM(0,N)模型的三元区间数序列预测

在工程和经济领域,很多数据序列具有很强的振荡性,这些振荡序列用区间数表示将包含更多信息.三元区间数不仅包含系统特征的上下界,还在中间增加一个偏好值,对三元区间数序列的预测研究具有很好的应用价值.为了使灰色模型GM(0,N)能够直接对三元区间数序列建模,改进了GM(0,N)模型方程的参数设置,将整体贡献系数和滞后项系数取为精确数,而将线性修正项系数和补偿系数设为三元区间数,从而对三元区间数的不同界点进行线性修正和补偿.进一步,为了提高对振荡序列的预测精度,结合马尔科夫预测和序列转换方法对模型的预测序列进行修正. 通过对我国用电量和社会消费品零售总额的预测,表明了所提出的三元区间数多变量灰色模型比单变量灰色模型和区间数序列转换为精确数序列再预测的方法效果更好.     

Forecasting time series using a methodology based on autoregressive integrated moving average and genetic programming

The autoregressive integrated moving average (ARIMA), which is a conventional statistical method, is employed in many fields to construct models for forecasting time series. Although ARIMA can be adopted to obtain a highly accurate linear forecasting model, it cannot accurately forecast nonlinear time series. Artificial neural network (ANN) can be utilized to construct more accurate forecasting model than ARIMA for nonlinear time series, but explaining the meaning of the hidden layers of ANN is difficult and, moreover, it does not yield a mathematical equation. This study proposes a hybrid forecasting model for nonlinear time series by combining ARIMA with genetic programming (GP) to improve upon both the ANN and the ARIMA forecasting models. Finally, some real data sets are adopted to demonstrate the effectiveness of the proposed forecasting model.     

Time-delay neural networks for time series prediction: an application to the monthly wholesale price of oilseeds in India

Agricultural price forecasting is one of the challenging areas of time series forecasting. The feed-forward time-delay neural network (TDNN) is one of the promising and potential methods for time series prediction. However, empirical evaluations of TDNN with autoregressive integrated moving average (ARIMA) model often yield mixed results in terms of the superiority in forecasting performance. In this paper, the price forecasting capabilities of TDNN model, which can model nonlinear relationship, are compared with ARIMA model using monthly wholesale price series of oilseed crops traded in different markets in India. Most earlier studies of forecast accuracy for TDNN versus ARIMA do not consider pretesting for nonlinearity. This study shows that the nonlinearity test of price series provides reliable guide to post-sample forecast accuracy for neural network model. The TDNN model in general provides better forecast accuracy in terms of conventional root mean square error values as compared to ARIMA model for nonlinear patterns. The study also reveals that the neural network models have clear advantage over linear models for predicting the direction of monthly price change for different series. Such direction of change forecasts is particularly important in economics for capturing the business cycle movements relating to the turning points.     

A moving-average filter based hybrid ARIMA–ANN model for forecasting time series data

A suitable combination of linear and nonlinear models provides a more accurate prediction model than an individual linear or nonlinear model for forecasting time series data originating from various applications. The linear autoregressive integrated moving average (ARIMA) and nonlinear artificial neural network (ANN) models are explored in this paper to devise a new hybrid ARIMA–ANN model for the prediction of time series data. Many of the hybrid ARIMA–ANN models which exist in the literature apply an ARIMA model to given time series data, consider the error between the original and the ARIMA-predicted data as a nonlinear component, and model it using an ANN in different ways. Though these models give predictions with higher accuracy than the individual models, there is scope for further improvement in the accuracy if the nature of the given time series is taken into account before applying the models. In the work described in this paper, the nature of volatility was explored using a moving-average filter, and then an ARIMA and an ANN model were suitably applied. Using a simulated data set and experimental data sets such as sunspot data, electricity price data, and stock market data, the proposed hybrid ARIMA–ANN model was applied along with individual ARIMA and ANN models and some existing hybrid ARIMA–ANN models. The results obtained from all of these data sets show that for both one-step-ahead and multistep-ahead forecasts, the proposed hybrid model has higher prediction accuracy.     

基于小波神经网络的非线性误差校正模型及其预测

针对非线性系统的预测问题，在线性和非线性协整理论涵义的基础上，提出利用小波神经网络进行非线性协整系统的非线性误差校正模型的研究，并给出该模型的建模方法．对沪深股市进行实证研究，与线性向量自回归模型进行比较．研究证明，小波神经网络所建立的非线性误差校正模型有较好的预测效果，能够有效地预测非线性经济系统．     

A hybrid neural network and ARIMA model for water quality time series prediction

Accurate predictions of time series data have motivated the researchers to develop innovative models for water resources management. Time series data often contain both linear and nonlinear patterns. Therefore, neither ARIMA nor neural networks can be adequate in modeling and predicting time series data. The ARIMA model cannot deal with nonlinear relationships while the neural network model alone is not able to handle both linear and nonlinear patterns equally well. In the present study, a hybrid ARIMA and neural network model is proposed that is capable of exploiting the strengths of traditional time series approaches and artificial neural networks. The proposed approach consists of an ARIMA methodology and feed-forward, backpropagation network structure with an optimized conjugated training algorithm. The hybrid approach for time series prediction is tested using 108-month observations of water quality data, including water temperature, boron and dissolved oxygen, during 1996–2004 at Büyük Menderes river, Turkey. Specifically, the results from the hybrid model provide a robust modeling framework capable of capturing the nonlinear nature of the complex time series and thus producing more accurate predictions. The correlation coefficients between the hybrid model predicted values and observed data for boron, dissolved oxygen and water temperature are 0.902, 0.893, and 0.909, respectively, which are satisfactory in common model applications. Predicted water quality data from the hybrid model are compared with those from the ARIMA methodology and neural network architecture using the accuracy measures. Owing to its ability in recognizing time series patterns and nonlinear characteristics, the hybrid model provides much better accuracy over the ARIMA and neural network models for water quality predictions.     

A hybrid optimized error correction system for time series forecasting

Time series forecasting is a challenging task in machine learning. Real world time series are often composed by linear and nonlinear structures which need to be mapped by some forecasting method. Linear methods such as autoregressive integrated moving average (ARIMA) and nonlinear methods such as artificial neural networks (ANNs) could be employed to handle such problems, however model misspecification hinders the forecasting process producing inaccurate models. Hybrid models based on error forecasting and combination can reduce the misspecification of single models and improve the accuracy of the system. This work proposes a hybrid system that is composed of three parts: a) linear modeling of the time series, b) nonlinear modeling of the error series, and c) combination of the forecasts using three distinct approaches. The system performs a search for the best parameters of the linear and nonlinear components, and of the combination approaches. Particle swarm optimization is used to find suitable architecture and weights. Experiments show that the proposed technique achieved promising results in time series forecasting.     

ARIMA模型在农产品价格预测中的应用

利用农产品价格时间序列的当前值和过去值准确预报未来值,将有利于正确引导农产品流通和农业生产,实现农产品区域供求平衡,并为政府和农户提供结构调整的依据。针对农产品价格这一重要问题,以白菜月价格数据为例,构建非平稳时间序列ARIMA（p,d,q）模型并预测白菜未来的月价格。结果表明ARIMA（0,1,1）模型能很好地模拟并预测白菜月价格趋势,为农产品市场信息的准确预测提供重要方法。     

一种基于SARIMA-LSTM模型的电网主机负载预测方法

随着智能电网的不断发展,如何提高对信息设备运行状态的预测准确率以及设置适应数据变化的动态阈值区间是电网IT运维面临的巨大挑战。为了解决这些问题,提出了组合时间序列预测模型(SARIMA-LSTM),即在传统周期性ARIMA 模型(SARIMA)的基础上,引入深度学习领域的LSTM模型,并摒弃了过去精度低、效果差的误差拟合方法,使用误差自回归方法来补偿预测结果。该模型可以学习到传统ARIMA模型无法捕捉到的误差波动规律,解决其无法预测非线性数据的问题。实验结果表明,在实际预测电网内存负载数据时,与ARIMA模型和SAIRIMA模型相比,SARIMA-LSTM模型可以实现更高的预测精度。