Single Time Series Model Research Articles

Deep Learning-Enabled Dynamic Model for Nutrient Status Detection of Aquaponically Grown Plants

Developing models to assess the nutrient status of plants at various growth stages is challenging due to the dynamic nature of plant development. Hence, this study encoded spatiotemporal information of plants within a single time-series model to precisely assess the nutrient status of aquaponically cultivated lettuce. In particular, the long short-term memory (LSTM) and deep autoencoder (DAE) approaches were combined to classify aquaponically grown lettuce plants according to their nutrient status. The proposed approach was validated using extensive sequential hyperspectral reflectance measurements acquired from lettuce leaves at different growth stages across the growing season. A DAE was used to extract distinct features from each sequential spectral dataset time step. These features were used as input to an LSTM model to classify lettuce grown across a gradient of nutrient levels. The results demonstrated that the LSTM outperformed the convolutional neural network (CNN) and multi-class support vector machine (MCSVM) approaches. Also, features selected by the DAE showed better performance compared to features extracted using both genetic algorithms (GAs) and sequential forward selection (SFS). The hybridization of deep autoencoder and long short-term memory (DAE-LSTM) obtained the highest overall classification accuracy of 94%. The suggested methodology presents a pathway to automating the process of nutrient status diagnosis throughout the entire plant life cycle, with the LSTM technique poised to assume a pivotal role in forthcoming time-series analyses for precision agriculture.

Modeling and Analysis of Monkeypox Outbreak Using a New Time Series Ensemble Technique

The coronavirus pandemic has raised concerns about the emergence of other viral infections, such as monkeypox, which has become a significant hazard to public health. Thus, this work proposes a novel time series ensemble technique for analyzing and forecasting the spread of monkeypox in the four highly infected countries with the monkeypox virus. This approach involved processing the first cumulative confirmed case time series to address variance stabilization, normalization, stationarity, and a nonlinear secular trend component. After that, five single time series models and three proposed ensemble models are used to estimate the filtered confirmed case time series. The accuracy of the models is evaluated using typical accuracy mean errors, graphical evaluation, and an equal forecasting accuracy statistical test. Based on the results, it is found that the proposed time series ensemble forecasting approach is an efficient and accurate way to forecast the cumulative confirmed cases for the top four countries in the world and the entire world. Using the best ensemble model, a forecast is made for the next 28 days (four weeks), which will help understand the spread of the disease and the associated risks. This information can prevent further spread and enable timely and effective treatment. Furthermore, the developed novel time series ensemble approach can be used to forecast other diseases in the future.

Domain knowledge decomposition of building energy consumption and a hybrid data-driven model for 24-h ahead predictions

The task of building energy prediction (BEP) is essential to several emerging research domains, including energy management, control optimization and fault detection. An accuracy prediction model contributes significantly to the improvement of building energy efficiency and flexibility. However, most existing prediction models are either based on single time series model or static model, making them perform poorly for long-term predictions. To solve this problem, this paper proposes a hybrid prediction model, which combines the deep ensemble (DE) model and autoregressive (AR) model together. Its motivation comes from the domain knowledge analysis of building energy consumption, where the energy consumption is decomposed into a global part and a local part. The deep ensemble model is adopted to predict its global part, and the AR model is employed to predict its local part. Comprehensive data experiments are conducted based on 50 real buildings across five building types to validate the model performance. The prediction horizon is the 24-h ahead sliding window prediction for one-year energy consumption. The results indicate that the hybrid prediction model outperforms the LSTM, DE-only, and ARIMA-only model, where its relative improvements of CV-RMSE are 28.7%, 35.98% and 18.47%, respectively. The experimental results also reveal that the types of buildings, i.e., office, lodging, will affect the model performance, which is attributed to their different user-behaviors. Based on the experimental results, it is demonstrated that the integration of static model and time series model is robust to the varying of building types and prediction steps, which should be the preferred choice of BEP task.

A Spatial Model for Repairing of the Dam Safety Monitoring Data Combining the Variable Importance for Projection (VIP) and Cokriging Methods

The safe operation of dams is related to the lifeline of the national economy, the safety of the people, and social stability, and dam safety monitoring plays an essential role in scientifically controlling the safety of dams. Since the effects of environmental variables were not considered in conventional monitoring data repairing methods (such as the single time series model and spatial interpolation model), a spatial model for repairing monitoring data combining the variable importance for projection (VIP) method and cokriging was put forward in this paper. In order to improve the accuracy of the model, the influence of different combinations of covariates on it was discussed, and the VIPj value greater than 0.8 was proposed as the threshold of covariates. The engineering verification shows that the VIP-cokriging spatial model had the advantages of high precision and strong applicability compared with the inverse distance weighting (IDW) model, the ordinary kriging model, and the universal kriging model, and the overall error can be reduced by more than 60%, which could better realize the expansion of the monitoring effect variable to the whole area of the dam space. The engineering application of the PBG dam showed that the model scientifically correlated the existing monitoring points with the spatial location of the dam, and reasonably repaired the measured values of the stopping and abnormal measured points, effectively ensuring that the spatial regular of the monitoring data could truly reflect the actual safety and operational status of the dam.

Temperature control load-regulation strategy based on multi-dimensional state queue method

The development of temperature control load is inevitable for people to improve the quality of life, but it has the same characteristics as the output of wind power generation, that is, randomness and intermittence. Therefore, the continuous access of temperature control load has an unprecedented challenge to the stability of power grid. However, the instability of the power grid caused by the input of wind power can be balanced by the temperature control load acting on the power grid. The research direction of this paper is the regulation of temperature controlled load household air source heat pump equipment on the power grid on the load side, which is constrained by meeting the user’s comfort. It is intended to achieve the goal of cutting peak and filling valley for the power grid side and reducing the wind rejection rate. The user side gets the economic compensation to cooperate with the power grid dispatching. At the same time, the power grid side improves the economic benefits by reducing the peak valley difference and reducing the occurrence of fan suspension. The main research contents are as follows: the thermodynamic equivalent model of single air source heat pump is established, and the aggregation model is established combined with Monte Carlo method. The simulation analysis shows that the air source heat pump group can participate in the regulation of load side; Using the prediction model of wind power based on meta learning, combined with single time series model and single linear regression model, the wind power is reasonably predicted; The multi-dimensional state queue method is used to improve the load characteristics and reduce the air rejection rate on the premise of meeting the user comfort; This paper expounds the distribution network level load regulation system, establishes the hierarchical load control structure, and makes the two-way interaction between the family compliance control system and the power grid a reality. In order to study the influence of user selection on regulation effect, according to the differences of user needs, we established three regulation strategies. Scheme A can achieve the most ideal regulation effect, and scheme B is slightly worse than scheme A. The most ideal control effect parameters can be obtained by scheme A, and the worst control effect parameters can be obtained by scheme C. Comparing the regulation effect parameters after scheme a and scheme C, based on the parameters of scheme C, the peak valley difference of scheme A is reduced by 18.30%, the peak to average ratio is reduced by 17.61% and the load variance is reduced by 18.12%. It is proved that the user can reduce the peak valley difference by using scheme A. Comparing the regulation effect parameters after scheme B and scheme C, compared with scheme C, the peak valley difference of scheme B is reduced by 11.80%, the peak to average ratio is reduced by 11.81%, and the load variance is reduced by 14.77%. It is proved that the regulation effect of scheme B can be reduced, but the effect is not as good as scheme A.

Prediction of Spot Price of Iron Ore Based on PSR-WA-LSSVM Combined Model

Aiming at the problems that the existing single time series models are not accurate and robust enough when it comes to forecasting the iron ore prices and the parameters of the traditional LSSVM model are difficult to determine, we propose a combined model based on Phase Space Reconstruction (PSR), wavelet transform and LSSVM (PSR-WA-LSSVM) to tackle these issues. ARIMA model, LSTM model, PSR-LSSVM model, and PSR-WA-LSSVM models were used for contrast simulation to forecast the spot price data of 61.5%PB powder from January 30, 2019, to February 1, 2021, in Ningbo Zhoushan port. The experimental results show that the PSR-WA-LSSVM combination model achieves better prediction results. At the same time, the model has a good performance in the multistep prediction of the iron ore price.

Nutrient Status Diagnosis of Infield Oilseed Rape via Deep Learning-Enabled Dynamic Model

The symptoms of the nutrient stress in plant canopies differ among different growth stages. It is a challenge to develop individual diagnosis models to evaluate the nutrient status for a specific growth stage in time. Therefore, this article encoded spatiotemporal information of plants in a single time-series model to evaluate the nutrient status of oilseed rape more efficiently. Specifically, in this article, we combined the convolutional neural network (CNN) and long short-term memory (LSTM) to classify the oilseed rape crops according to their nutrient status. The model was validated on a large number of sequential images acquired from oilseed rape canopies at different growth stages during a two-year experiment. Different pretrained CNNs were used to extract distinctive features from every time step of sequential images and then, these features were considered as the input of LSTM to classify the oilseed rape into nine classes of nutrient statuses. We demonstrated that the LSTM outperformed the traditional machine-learning method and the deep features showed better performance compared with hand-crafted features. The Inceptionv3-LSTM obtained the highest overall classification accuracy of 95% when tested on the dataset of 2017/2018 and it also provided a good generalization when using a cross-dataset validation, with the highest overall accuracy of 92%. Our proposed approach presents a pathway toward automatic nutrient status diagnosis during the whole life cycle of the plants, and the LSTM technique would play an essential role in the near future for time-series analysis for precision agriculture.

Prediction on China’s Energy Consumption Demand Trend Based on Bayesian Theorem

This paper applies SAS software for data mining and takes the energy consumption data from 2000 to 2015 as the training set and the corresponding data from 2016 to 2017 as comparative set. Due to the exponentially fluctuation of data, the author sets a nonlinear modified exponential rudimentary model, which, when combined with white noise experiment and diagrammatic figure analysis, the model’s fitting effect can be inferred. The result shows that the data extraction of the model is not sufficient enough so that the model should be improved. Seen in that light, the author tries to do dimensionality reduction and differential processing to the original sequence and applies the classical time series ARIMA model for adjustment. It comes out that the fitted model should be ARIMA(4,2,1). The solved result illustrates that the information of the model has been well and sufficiently extracted. After that, the Bayesian model is constructed by taking the estimated parameters of the time series ARIMA model as priori information. The prediction effects of the three models are finally evaluated by Mean Absolute Percentage Error (MAPE) and Root-mean-square Error (RMSE). The result shows that the prediction effect of Bayesian model is somehow superior to that of single time series model. According to the prediction result of Bayesian model based on prior robust, the general demand for energy consumption in China during the 13th Five-Year Plan period tends to climb by a small margin.

How Strong is the Relationship Among Gold and USD Exchange Rates? Analytics Based on Structural Change Models

This study examines the dynamic relationship among gold and USD exchange rates. Since one single time series model can suffer from structural (or parameter) changes in underlying models, we consider those models with structural breaks. We first employ the cumulative sum of squared residual test to determine the number and locations of change points in the volatility of time series and then divide the whole period by the change points to investigate the relationship between gold and USD exchange rates in each sub-period, based on the time-varying correlations obtained from dynamic conditional correlation models. We show that a negative correlation exists in almost all periods and that the correlation coefficients have higher absolute values during the global financial crisis period than in other periods. Furthermore, the correlation becomes much greater along with downside moves of USD versus upside moves, indicating that a depreciating trend of USD typically has more influence on gold than an appreciating trend. This phenomenon is in line with the leverage effect in financial markets. After comparing the two methods of with/without structural changes, our findings from an empirical study provide evidence that ignoring structural changes can lead to a false conclusion and confirm that our method offers a functional tool to analyze gold prices and USD exchange rates.

An Improved PLS based Time-Varying Weight Combination Approach

Sales forecasting with double trends time series (long-term trend and seasonal volatility) has been challenging due to its complexity. Neither single time series model nor fixed weight combination approach can fully capture the comprehensive information. We address the issue by proposing an improved partial least squares (PLS) based time-varying weight combination approach. The proposed method overcomes the problems by handling both the relation between single models involved and the relation between single models and time ordering with time-varying weights and PLS approach. We applied it in three data sets. The results show that our new model is the best among the existing models. It can significantly improve both data fitness and prediction accuracy over the existing single and combination methods.

Improving short‐term traffic forecasts: to combine models or not to combine?

This study compares the performance of statistical and Bayesian combination models with classical single time series models for short-term traffic forecasting. Combinations are based on fractionally integrated autoregressive time series models of travel speed with exogenous variables that consider speed's spatio-temporal evolution, and volume and weather conditions. Several statistical hypotheses on the effectiveness of combinations compared to the single models are also tested. Results show that, in the specific application, linear regression combination techniques may provide more accurate forecasts than Bayesian combination models. Moreover, combining models with different degrees of spatio-temporal complexity and exogeneities is most likely to be the best choice in terms of accuracy. Moreover, the risk of combining forecasts is lower than the risk of choosing a single model with increased spatio-temporal complexity.

The application of weighted combination forecasting model for health resource allocation

Scientificly forecasting the health resources is the premise and foundation for making health resource planning.This paper summarized the application scope and characteristics of commonly used statistical models for health resources forecasting,introduced many S type curve prediction models commonly used in natural and social economic fields,hackled and concluded the weight calculation methods of combination forecasting models,and on this basis put forward that multivariable time series model or combination forecasting model based on single time series model and multi-linear regression equation of the predictive value should be set up for forecasting health resources,so as to provide methodological references for related forecasting research. Key words: Weighted; Combination forecasting model; Health resources

Forecasting Time Series With Long Memory and Level Shifts: A Bayesian Approach

Recent studies have showed that it is troublesome, in practice, to distinguish between long memory and nonlinear processes. Therefore, it is of obvious interest to try to capture both features of long memory and non-linearity into a single time series model to be able to assess their relative importance. In this paper we put forward such a model, where we combine the features of long memory and Markov nonlinearity. A Markov Chain Monte Carlo algorithm is proposed to estimate the model and evaluate its forecasting performance using Bayesian predictive densities. The resulting forecasts are a significant improvement over those obtained by the linear long memory and Markov switching models.

WHO IS AFRAID OF MISSING DATA IN SPECTRAL ANALYSIS

The program ARMAsel automatically selects a single time series model for given stationary stochastic data. Three model types are candidates for selection. The type can be AR or autoregressive, MA or moving average and the combined ARMA type. The parameters of that selected model accurately represent the power spectral density and the autocorrelation function of the data. The reduced statistics ARMAsel-rs algorithm uses a long AR model as only input to compute models of the other types and to select the best. ARMAsel-mis is a new program that can handle missing data. It computes AR models with a numerically stable maximum likelihood algorithm and uses ARMAsel-rs to determine MA and ARMA models. The order and the type of the best candidate are automatically selected with dedicated missing data criteria, supposing that data are randomly missing. Missing less than 10 % of the data has not much influence on the accuracy, missing 50 % generally looses a factor two in the accuracy in comparison with the model estimated from the same number of contiguous observations. Low order time series models can be computed as long as the product of the remaining fraction and the remaining number of observations is greater than about 10.

A comparative study of autoregressive neural network hybrids

Many researchers have argued that combining many models for forecasting gives better estimates than single time series models. For example, a hybrid architecture comprising an autoregressive integrated moving average model (ARIMA) and a neural network is a well-known technique that has recently been shown to give better forecasts by taking advantage of each model's capabilities. However, this assumption carries the danger of underestimating the relationship between the model's linear and non-linear components, particularly by assuming that individual forecasting techniques are appropriate, say, for modeling the residuals. In this paper, we show that such combinations do not necessarily outperform individual forecasts. On the contrary, we show that the combined forecast can underperform significantly compared to its constituents’ performances. We demonstrate this using nine data sets, autoregressive linear and time-delay neural network models.

Facts and fiction in spectral analysis

This analysis is limited to the spectral analysis of stationary stochastic processes with unknown spectral density. The main spectral estimation methods are: parametric with time series models, or nonparametric with a windowed periodogram. A single time series model will be chosen with a statistical criterion from three previously estimated and selected models: the best autoregressive (AR) model, the best moving average (MA) model, and the best combined ARMA model. The accuracy of the spectrum, computed from this single selected time series model, is compared with the accuracy of some windowed periodogram estimates. The time series model generally gives a spectrum that is better than the best possible windowed periodogram. It is a fact that a single good time series model can be selected automatically for statistical data with unknown spectral density. It is fiction that objective choices between windowed periodograms can be made.

Predicting Winter Flounder (Pseudopleuronectes americanus) Catches by Time Series Analysis

Long-term changes in catches of winter flounder (Pseudopleuronectes americanus) were compared at five locations within Narragansett Bay, Rhode Island, USA and in three areas directly offshore. In the lower Bay, relative abundance decreased 86% during warming in the early 1970's, recovered briefly, and then declined to an all-time low in 1986. Time-series analysis successfully predicted a recovery in 1987. Year ahead predictions, based on annual models, had a maximum error of 14% (1986); monthly forecasts explained 62–72% of catch variation during the entire investigation; cumulative monthly error (forecast compared with actual over an annual cycle) was 11% in the Bay and 21% offshore. Commercial offshore catch and the fish retained on power plant intake screens in two major rivers showed the same 11-yr pattern of population fall and recovery detected in the lower Bay. Coherent behavior was also demonstrated by the similarity of random processes which generated the time-series data. Thus a single time-series model for southern New England winter flounder seems feasible.

Model Selection for Multi-Step-Ahead Forecasting

Abstract It is a common practice to fit a single time series model for the purpose of forecasting an observed time series at several different leads. By examining autoregressivc models, we demonstrate both theoretically and empirically that there are situations in which better forecasts can be obtained by using a different “model” for each lead.