Time Series Prediction Method Research Articles

Prediction of Parking Space Availability Using Improved MAT-LSTM Network

The prediction of parking space availability plays a crucial role in information systems providing parking guidance. However, controversy persists regarding the efficiency and accuracy of mainstream time series prediction methods, such as convolutional neural networks (CNNs) and recurrent neural networks (RNNs). In this study, a comparison was made between a temporal convolutional network (TCN) based on CNNs and a long short-term memory (LSTM) network based on RNNs to determine an appropriate baseline for predicting parking space availability. Subsequently, a multi-head attention (MAT) mechanism was incorporated into an LSTM network, attempting to improve its accuracy. Experiments were conducted on three real and two synthetic datasets. The results indicated that the TCN achieved the fastest convergence, whereas the MAT-LSTM method provided the highest average accuracy, namely 0.0330 and 1.102 × 10−6, on the real and synthetic datasets, respectively. Furthermore, the improved MAT-LSTM model accomplished an increase of up to 48% in accuracy compared with the classic LSTM model. Consequently, we concluded that RNN-based networks are better suited for predicting long-time series. In particular, the MAT-LSTM method proposed in this study holds higher application value for predicting parking space availability with a higher accuracy.

Sea Surface Temperature Prediction in China Sea Based on SAM-LSTM Approach

Sea Surface Temperature (SST), a critical environmental element in the ocean, significantly impacts the global atmosphere-ocean energy balance and holds the potential to trigger severe weather like droughts, floods, and El Niño events. Therefore, the prediction of future SST dynamics is crucial to identifying these extreme events and mitigating the damage they caused. In this study, we introduce a time series prediction method based on the Self-Attention Mechanism-Long Short-Term Memory (SAM-LSTM) model. In addition, the historical time-series satellite data of SST anomaly (SSTA) is used instead of SST itself considering that the fluctuations of SST are very small compared to their absolute magnitudes. The Seasonal-Trend decomposition using Loess (STL) method is adopted to decompose the complex non-linear SSTA time series into trend components, seasonal components, and residual components. Then, the deseasonalized time series data at 6 locations in the Bohai Sea are used to train and valid the developed SAM-LSTM model. After that, the validated models are applied to the Yellow Sea, East China Sea, and South China Sea. The experimental results show that the combination of STL time series decomposition and SAM-LSTM can achieve high-precision prediction of daily SSTA than LSTM. This suggests that the methodology used in this paper has a good application for short-term daily SST prediction.

Comment on papers using machine learning for significant wave height time series prediction: Complex models do not outperform auto-regression

Significant Wave Height (SWH) is crucial in many aspect of ocean engineering. The accurate prediction of SWH has therefore been of immense practical value. Recently, Artificial Intelligence (AI) time series prediction methods have been widely used for single-point short-term SWH time-series forecasting, resulting in many AI-based models claiming to achieve good results. However, the extent to which these complex AI models can outperform traditional methods has largely been overlooked. This study compared five different models - AutoRegressive (AR), eXtreme Gradient Boosting (XGB), Artificial Neural Network (ANN), Long Short-Term Memory (LSTM), and WaveNet - for their performance on SWH time series prediction at 16 buoy locations. Surprisingly, the results suggest that the differences of performance among different models are negligible, indicating that all these AI models have only “learned” the linear auto-regression from the data. Additionally, we noticed that many recent studies used signal decomposition method for such time series prediction, and most of them decomposed the test sets, which is WRONG.

Learning from Polar Representation: An Extreme-Adaptive Model for Long-Term Time Series Forecasting

In the hydrology field, time series forecasting is crucial for efficient water resource management, improving flood and drought control and increasing the safety and quality of life for the general population. However, predicting long-term streamflow is a complex task due to the presence of extreme events. It requires the capture of long-range dependencies and the modeling of rare but important extreme values. Existing approaches often struggle to tackle these dual challenges simultaneously. In this paper, we specifically delve into these issues and propose Distance-weighted Auto-regularized Neural network (DAN), a novel extreme-adaptive model for long-range forecasting of stremflow enhanced by polar representation learning. DAN utilizes a distance-weighted multi-loss mechanism and stackable blocks to dynamically refine indicator sequences from exogenous data, while also being able to handle uni-variate time-series by employing Gaussian Mixture probability modeling to improve robustness to severe events. We also introduce Kruskal-Wallis sampling and gate control vectors to handle imbalanced extreme data. On four real-life hydrologic streamflow datasets, we demonstrate that DAN significantly outperforms both state-of-the-art hydrologic time series prediction methods and general methods designed for long-term time series prediction.

A process knowledge-based hybrid method for univariate time series prediction with uncertain inputs in process industry

In process industry, accurate and efficient univariate time series prediction (TSP) of key process parameters is crucial for optimizing production processes. However, noisy data and varying working conditions introduce uncertainties that hinder the modeling process. Data-driven methods have been proposed to address input-uncertain TSP models, but they often fail to obtain sufficient and high-quality industrial data for model establishment in complex and dynamic production process. The paper presents a process knowledge-based hybrid method for univariate TSP that handles noisy inputs in different working conditions. Primarily, a knowledge-embedded deep kernel network (KE-DKN) model is proposed that approximates the outputs to the knowledge space in noisy industrial environment, by embedding instantiated process knowledge as soft constraints into the conventional DKN network. Further, a two-stage training schema is designed for KE-DKN to reduce the interference superposition on the predicted variable from random noisy data and operational interferences. Moreover, the pre-trained parameters transferring is conducted from stable working conditions to mitigate the low modeling efficiency in unstable working conditions. In two cases from process industry, the proposed method delivers higher prediction accuracy and robustness than baseline methods in both working conditions, and the knowledge transfer significantly enhances the modeling efficiency in unstable working conditions.

Assessing and predicting indoor environmental quality in 13 naturally ventilated urban residential dwellings

This research presents the assessments of IEQ in 13 naturally ventilated urban residential dwellings based on the long-term monitoring of CO2, PM2.5, formaldehyde, temperature, and relative humidity within a year. In addition to the costly monitoring techniques, we utilized time series prediction methods as an economical and efficient approach to comprehend the quality of indoor environment. Nevertheless, choosing a prediction method proves to be a challenge due to the inconclusive nature of the advantages and disadvantages associated with various methodologies. Thus, we conducted a comprehensive comparison of the LSTM, SVM, ARIMA, and BPNN algorithms for predicting IEQ. The results indicate that for short-term prediction (one day), the SVM algorithm has the shortest fitting time, the program occupies the least memory, and the overfitting problem is not readily apparent, while the prediction accuracy is acceptable. Long-term (one year) predictions can be executed with greater precision using LSTM. In regard to robustness, prediction time and accuracy of SVM in different dwellings varies greatly and the robustness is weak. When considering parameters, the discrepancy between the prediction error of PM2.5 and the sensor error is more pronounced, resulting in an even lower level of predictability. As for the input data, it was observed that the computation time of the four algorithms increased linearly with the sample size. While the accuracy of predictions is predominantly influenced by the sampling frequency of the original data. The investigation results will benefit future decision-making and control strategies regarding the enhancement of thermal comfort and indoor air quality.

A Time Series Prediction Method for the Subsurface Thermal Structure of the South Yellow Sea Cold Water Mass

A Time Series Prediction Method for the Subsurface Thermal Structure of the South Yellow Sea Cold Water Mass

Deep Learning for Time-Series Prediction in IIoT: Progress, Challenges, and Prospects.

Time-series prediction plays a crucial role in the Industrial Internet of Things (IIoT) to enable intelligent process control, analysis, and management, such as complex equipment maintenance, product quality management, and dynamic process monitoring. Traditional methods face challenges in obtaining latent insights due to the growing complexity of IIoT. Recently, the latest development of deep learning provides innovative solutions for IIoT time-series prediction. In this survey, we analyze the existing deep learning-based time-series prediction methods and present the main challenges of time-series prediction in IIoT. Furthermore, we propose a framework of state-of-the-art solutions to overcome the challenges of time-series prediction in IIoT and summarize its application in practical scenarios, such as predictive maintenance, product quality prediction, and supply chain management. Finally, we conclude with comments on possible future directions for the development of time-series prediction to enable extensible knowledge mining for complex tasks in IIoT.

Research on Ensemble Learning-Based Feature Selection Method for Time-Series Prediction

Feature selection has perennially stood as a pivotal concern in the realm of time-series forecasting due to its direct influence on the efficacy of predictive models. Conventional approaches to feature selection predominantly rely on domain knowledge and experiential insights and are, therefore, susceptible to individual subjectivity and the resultant inconsistencies in the outcomes. Particularly in domains such as financial markets, and within datasets comprising time-series information, an abundance of features adds complexity, necessitating adept handling of high-dimensional data. The computational expenses associated with traditional methodologies in managing such data dimensions, coupled with vulnerability to the curse of dimensionality, further compound the challenges at hand. In response to these challenges, this paper advocates for an innovative approach—a feature selection method grounded in ensemble learning. The paper explicitly delineates the formal integration of ensemble learning into feature selection, guided by the overarching principle of “good but different”. To operationalize this concept, five feature selection methods that are well suited to ensemble learning were identified, and their respective weights were determined through K-fold cross-validation when applied to specific datasets. This ensemble method amalgamates the outcomes of diverse feature selection techniques into a numeric composite, thereby mitigating potential biases inherent in traditional methods and elevating the precision and comprehensiveness of feature selection. Consequently, this method improves the performance of time-series prediction models.

An optimization in big data time series prediction method by Parzen estimation with Spark

With the development and change of big data related technologies, more and more large amounts of data need to be analyzed. Now there are companies like Google, Yahoo, etc. Frameworks such as MapReduce, Hadoop, Spark, etc. are developed for processing large amounts of data. In this paper, relevant discussions and researches are carried out on time series forecasting under the new era of big data. Now there are time series forecasting methods based on map reduce, Hadoop, spark data processing framework, including nearest neighbor distribution method, neural network method, etc., which have made quite good achievements in big data time series forecasting. By reading the relevant research literature, it is universally acknowledged that the Sparks framework has good application prospects and potential in predicting big data time series. As a result, this paper is mainly aimed at the optimization and improvement of the big data time series forecasting method on the basis of the spark framework. The author noticed that most of the default configurations of spark clusters are generated by default or automatically, rather than the optimal solution obtained after algorithm optimization, so there is still room for improvement in this regard. In this regard, this paper proposes a kernel method for visual data processing of related configurations and parameters, and then optimizes the default data configuration as much as possible to improve the accuracy and feasibility of the big data time series prediction method on the basis of the spark framework. In this paper, the optimized scheme is used to forecast the domestic electricity consumption in the past five years, and the results show that the optimized scheme has a good improvement performance on the basis of the original method.

A WSFA-based adaptive feature extraction method for multivariate time series prediction

A WSFA-based adaptive feature extraction method for multivariate time series prediction

Modeling of Soil Moisture Data by ARMA Time Series

The use of known data to predict future environmental parameters plays a crucial role in agriculture. In this paper, we propose a novel time series prediction method that combines the Auto-Regressive Moving Average Model (ARMA) and Gradient Boost Regression Tree(GBR) to forecast future soil moisture values. Firstly, the optimal number of decomposition modes for AMRA is determined by using Auto-correlation Function (ACF) and Partial Auto-correlation Function (PACF) plots. Secondly, according to statistics of XIlin Gol grassland offered by the Huawei Cup Mathematical Modeling Contest in 2022, the data including soil evaporation, precipitation, and soil moisture in the past ten years, are used as input parameters of ARMA to predict the precipitation and soil evaporation from 2022 to 2023. Then, the superiority of GBR was verified by comparing algorithms such as Support Vector Regression (SVR) and Random Forest(RF). Finally, GBR was used to realize the prediction for different soil moisture values from 2022 to 2023.

Decomposition strategy and attention-based long short-term memory network for multi-step ultra-short-term agricultural power load forecasting

Accurate forecasting of the agricultural power load is important for rural electricity networks’ safe and stable operation. But it is more uncertain and difficult to forecast than industrial, commercial, and residential loads. Multivariate time series prediction methods have problems such as poor refinement of weather services and difficulty in quantifying the influencing factors. Univariate time series prediction methods should be emphasized, and the characteristic information in the historical load data should be fully exploited. Therefore, this paper systematically investigates the decomposition strategy and attention-based recurrent neural network for univariate time-series multi-step prediction. The decomposition strategy is employed to generate derived variable sequences from the target sequence as inputs for the model, while the dual-stage attention mechanism is used for dynamically selecting features. A one-year real-world dataset of agricultural power loads in southern China is used for validation. The effectiveness of two different decomposition algorithms on dual-stage attention-based recurrent neural network (DARNN) is discussed, then the proposed ensemble empirical mode decomposition and dual-stage attention-based recurrent neural network (EEMD-DARNN) is compared with four univariate time series prediction models and three ablation models. The proposed model is optimal in four evaluation indicators, with R2, RMSE, MAPE, and MAE of 0.970, 12.298, 0.040, and 7.335 respectively in the shortest forecast horizon (15 min), and 0.941, 17.305, 0.065 and 11.347 respectively in the longest forecast horizon (1 h). It is a significant improvement over the existing models. In conclusion, EEMD-DARNN can achieve more accurate ultra-short-term agricultural power load forecasting.

Multiple feature fusion transformer for modeling penicillin fermentation process with unequal sampling intervals.

The quality prediction of batch processes is an important task in the field of biological fermentation. However, dynamic nonlinearity, unequal sampling intervals, uneven duration, and multiple features of a batch process make this task challenging. Thus, the multiple-feature fusion transformer (MFFT) model is proposed for the time series quality prediction of a batch process. First, the application of sequence-to-sequence architecture enables MFFT to perform a wide range of sequence prediction tasks. Second, the transformer parallel operation model imposes no rigid requirement for the order of sequence input, allowing the model to deal with problems of unequal interval sampling and utilize the sequence information. Third, MFFT integrates a pretrained ResNet50 as a mycelium status classifier for fusing image information into the features. Moreover, a multiple-feature encoding structure is proposed to integrate sampling time and mycelium status. Finally, multiple tasks in penicillin fermentation have shown that MFFT significantly outperforms existing methods for time series prediction.

Seepage Prediction Model for Roller-Compacted Concrete Dam Using Support Vector Regression and Hybrid Parameter Optimization

In this study, a seepage prediction model was established for roller-compacted concrete dams using support vector regression (SVR) with hybrid parameter optimization (HPO). The model includes data processing via HPO and machine learning through SVR. HPO benefits from the correlation extraction capability of grey relational analysis and the dimensionality reduction technique of principal component analysis. The proposed model was trained, validated, and tested using 22 years of monitoring data regarding the Shuidong Dam in China. We compared the performance of HPO with other popular methods, while the SVR method was compared with the traditional time-series prediction method of long short-term memory (LSTM). Our findings reveal that the HPO method proves valuable real-time dam safety monitoring during data processing. Meanwhile, the SVR method demonstrates superior robustness in predicting seepage flowrate post-dam reinforcement, compared with LSTM. Thus, the developed model effectively identifies the factors related to seepage and exhibits high accuracy in predicting fluctuation trends regarding the Shuidong Dam, achieving a determination coefficient R2 > 0.9. Further, the model can provide valuable guidance for dam safety monitoring, including diagnosing the efficacy of monitoring parameters or equipment, evaluating equipment monitoring frequency, identifying locations sensitive to dam seepage, and predicting seepage.

Singular spectrum analysis (SSA) based hybrid models for emergency ambulance demand (EAD) time series forecasting

Abstract Accepted by: Konstantinos Nikolopoulos One of the challenges of emergency ambulance demand (EAD) time series prediction lies in their non-stationary nature. We study this important problem and propose two hybrid forecasting models, which combine the singular spectrum analysis (SSA) time-series technique with autoregressive integrated moving average (ARIMA) parameterized multivariate forecasting. Both daily and hourly time series are studied. The non-stationary time series are decomposed into three eigentriples by SSA: trends, periodic components and residuals. Selection of the group boundary point of the periodic component is a key issue in the SSA method. We use spectrum analysis to compute a threshold for maximum information content of periodic components. ARIMA mean value prediction models are employed to forecast the trends, periodic components and residuals sub-series. Our research compares ARIMA and SSA-based hybrid models by considering the emergency dispatching departure records of six core districts in Guangzhou city from 1 January 2021 to 31 December 2021. Results show that the integrated SSA-ARIMA model performs best. SSA is a very effective pre-processing method for non-stationary time series prediction. The predictive accuracy of using a hybrid model for hourly EAD time series is higher than that for daily ones. Our discussion should be useful for improving EAD prediction in contexts others than that considered in our research.

A hybrid neural network based model for blood donation forecasting

ObjectiveTo develop a hybrid neural network-based blood donation prediction method, via this predictive model, we can obtain the best estimate of whole blood in Beijing Tongzhou District Central Blood Station and help managers smoothly solve the allocation problem under fluctuating hospital demand and limited resources. MethodInspired by the practical problems faced by blood stations providing transfusion services to several hospitals, a hybrid model based on a time-series prediction method and neural network, SARIMAX-TCN-LSTM is proposed for the prediction of daily whole blood donations. The experiment was performed at the central blood station in Tongzhou district, where we used whole blood donations from January 1, 2015, to November 14, 2021, as the subject, supplemented by meteorological and epidemic factors affecting blood donation, to predict daily blood donations for the next two weeks. ResultThe hybrid model significantly outperformed the traditional time series forecasting method on multiple regression metrics, with twice as effective fitting as the baseline and a 33% reduction in Root Mean Squared Error (RMSE). Results indicate that the proposed model can improve the prediction accuracy of daily blood donations, and the co-validity of the structure was evidenced in an ablation experiment. ConclusionDevelopment and evaluation of a hybrid neural network-based model structure improve the prediction of daily blood donations. This intelligent forecasting method can help managers to overcome the challenges of sudden blood demand and contribute to the optimization of resource allocation tasks.

Analysis and Prognostication of Residents' Per Capita Disposable Income in Hubei Province using Time Series Prediction Methods

To anticipate the fluctuations in per capita disposable income among Hubei Province inhabitants for the subsequent biennium, a dataset spanning from 2005 to 2022 was culled. Employed in this study were three distinct time series prognostication methodologies: Exponential Smoothing (Holt-Winter), Autoregressive Moving Average (ARMA), and Autoregressive Integrated Moving Average (ARIMA). These techniques were applied to envision the forthcoming trajectory of per capita disposable income for the province's residents. By computing diverse metrics to assess predictive discrepancieslike the Mean Absolute Error (MAE) and Root Mean Square Error (RMSE)the effectiveness of the assorted models was gauged, culminating in the selection of the ARIMA model due to its superior performance. Capitalizing on this, approximations for per capita disposable income during 2023 and 2024 were extrapolated. The resultant prognoses project a sustained and noteworthy uptick in per capita disposable income for urban denizens of Hubei Province in the forthcoming biennial span. Ultimately, the findings were translated into actionable policy suggestions and deductions, rendering them highly pertinent for the dissection of Hubei Province's economic evolution.

A novel interval dual convolutional neural network method for interval-valued stock price prediction

Accurate interval-valued stock price prediction is challenging and of great interest to investors and for-profit organizations. In this study, by considering individual stock information and relevant stock information simultaneously, we propose a novel interval dual convolutional neural network (Dual-CNNI) model based method to predict interval-valued stock prices. First, the individual and relevant stock information are collected and transformed into images. Then, the Dual-CNNI model is proposed to predict interval-valued stock prices. Specifically, two convolutional neural network (CNN) models with different structures are constructed to respectively extract individual stock features and relevant stock features, and then an interval multilayer perceptron (MLPI) model is used for final interval-valued stock price prediction. Finally, extensive experiments are conducted based on six randomly selected stocks, with comparison to several popular machine learning model based methods and interval-valued time series (ITS) prediction methods. The experimental results indicate that the proposed Dual-CNNI based method has superior predictive ability.

Robust Interval Prediction of Intermittent Demand for Spare Parts Based on Tensor Optimization.

Demand for spare parts, which is triggered by element failure, project schedule and reliability demand, etc., is a kind of sensing data to the aftermarket service of large manufacturing enterprises. Prediction of the demand for spare parts plays a crucial role in inventory management and lifecycle quality management for the aftermarket service of large-scale manufacturing enterprises. In real-life applications, however, demand for spare parts occurs randomly and fluctuates greatly, and the demand sequence shows obvious intermittent distribution characteristics. Additionally, due to factors such as reporting mistakes made by personnel or environmental changes, the actual data of the demand for spare parts are prone to abnormal variations. It is thus hard to capture the evolutional pattern of the demand for spare parts by traditional time series forecasting methods. The reliability of prediction results is also reduced. To address these concerns, this paper proposes a tensor optimization-based robust interval prediction method of intermittent time series for the aftersales demand for spare parts. First, using the advantages of tensor decomposition to effectively mine intrinsic information from raw data, a sequence-smoothing network based on tensor decomposition and a stacked autoencoder is proposed. Tucker decomposition is applied to the hidden features of the encoder, and the obtained core tensor is reconstructed through the decoder, thus allowing us to smooth outliers in the original demand sequence. An alternating optimization algorithm is further designed to find the optimal sequence feature representation and tensor decomposition factors for the extraction of the evolutionary trend of the intermittent series. Second, an adaptive interval prediction algorithm with a dynamic update mechanism is designed to obtain point prediction values and prediction intervals for the demand sequence, thereby improving the reliability of the forecast. The proposed method is validated using the actual aftersales data from a large engineering manufacturing enterprise in China. The experimental results demonstrate that, compared with typical time series prediction methods, the proposed method can effectively grab the evolutionary trend of various intermittent series and improve the accuracy of predictions made with small-sample intermittent series. Moreover, the proposed method provides a reliable elastic prediction interval when distortion occurs in the prediction results, offering a new solution for intelligent planning decisions related to spare parts in practical maintenance.