Time Series Estimation Research Articles

A Factor Analysis Backpropagation Neural Network Model for Vegetation Net Primary Productivity Time Series Estimation in Western Sichuan

Vegetation net primary productivity (VNPP) is the main factor in ecosystem carbon sink function and regulation of environmental processes. However, NPP data products have data missing in some areas, which affects the availability and overall accuracy level of data. Therefore, we adopted the Factor Analysis Backpropagation neural network model (FA-BP model) to acquire a high-accuracy and high-reliability NPP result without missing or empty areas by using a series of easily accessible datasets, such as meteorological data and remote sensing data. We selected the western Sichuan region as the study area and carried out a VNPP time series estimation from 2000 to 2016. Comparative simulations also verify the accuracy of the time series estimation results: The Pearson correlation r of VNPP prediction results ranged from 0.807 to 0.917, the mean absolute error ranged from 29.1 to 38.9, the root mean square error was between 37.3 and 51.8, and the mean relative error varies from 0.10 to 0.14. Further analysis shows that the spatial pattern of estimated VNPP during the past 17 years in western Sichuan shows a decreasing trend from southeast to northwest. Besides, the VNPP time series is generally on an upward trend in this period. The increasing and decreasing areas of VNPP values in the study area accounted for 81.42% and 18.58%, respectively. Moreover, we find that temperature dominates the change of VNPP in the whole western Sichuan region. The data processing method and experimental results presented in this paper can provide a reference for accurately acquiring VNPP and related studies on natural resources and climate change.

The Size and Spatial Extent of Neighborhood Price Impacts of Infill Development: Scale Matters?

Infill investments are argued to mitigate environmental footprints, regenerate places and accommodating population growth, but frequently generate local opposition. However, there is a dearth of knowledge around how different types of infill affect different segments of local property markets, how persistent effects are and how far they reach. Using detailed geocoded infill development activity and sales data, we test the price level and trajectory impacts of five infill types, distinguished by the net scale of additional dwellings, on property prices within five concentric 100-meter rings. Using an adjusted interrupted time-series estimation strategy with locality, property and neighborhood characteristic controls we find that developments that generate a net increase in dwellings of four or less, typically result in an appreciation in the average sales prices of proximate dwellings. Moderate and large-scale developments generate negative price effects, but these effects predominantly affect apartments and townhouses, not the dominant detached house submarket. Over time, amenity effects and local market potential may even have a further positive expectation effect on detached house prices. Infill type differentiation shows that urban densification may result in positive affordability outcome in the apartment submarket, but has the opposite effects in the detached house submarket. Divergent price trajectories also contribute to widening wealth disparities.

Generalized Gaussian quasi-maximum likelihood estimation for most common time series

We propose a consistent estimator for the parameter shape of the generalized gaussian noise in the class of causal time series including ARMA, AR(∞), GARCH, ARCH(∞), ARMA-GARCH, APARCH, ARMA-APARCH,…, processes. As well we prove the consistency and the asymptotic normality of the Generalized Gaussian Quasi-Maximum Likelihood Estimator (GGQMLE) for this class of causal time series with any fixed parameter shape, which over-performs the efficiency of the classical Gaussian QMLE. Monte Carlo experiments confirm that the accuracy of the proposed estimators.

Exploring the nature of EKC hypothesis in Asia's top emitters: role of human capital, renewable and non-renewable energy consumption.

The present study uses both carbon dioxide emission and ecological footprints as proxies for environmental degradation to examine the environmental Kuznets curve hypothesis for the top three emitters from Asia, i.e., China, India, and Japan. To this end, the autoregressive distributed lag model for time series and panel estimation is used for a period spanning over 1980-2016. For carbon dioxide emission, China presents an inverted-U shape of the environmental Kuznets curve, while a U-shape relationship is found for India and Japan. Similarly, when the hypothesis is tested with the ecological footprint, Japan offers an inverted U-shape and U-shaped association is detected for China and India. The panel analysis indicates the existence of the environmental Kuznets curve with both proxies of environmental degradation. Besides, human capital and renewable energy promote environmental sustainability, while non-renewable energy use hinders environmental quality. The findings of this study suggest that in order to meet the combined goals of economic growth and environmental protection, the three economies, i.e., China, India, and Japan, should employ renewable energy-enabled technology.

Online state of health estimation for lithium-ion batteries based on a dual self-attention multivariate time series prediction network

With the development of cloud and edge computing, deep learning based on big data has been widely utilized for lithium-ion battery state of health (SoH) online estimation, where improving the accuracy, robustness, and real-time applicability are current research challenges. Focusing on these points, this paper proposes a novel health feature analysis and screening method and a dual self-attention multivariate time series estimation network (DSMTNet). First, the correlation between all feature sequences and the SoH is evaluated by the Pearson correlation coefficient method. On this basis, the 15 most relevant features are selected by the light gradient boosting machine method as the DSMTNet input. Next, multi-head convolutional neural networks are utilized for encoding the battery features to enhance the final representation learning results. Then, a global attention unit is utilized to model the weights of the encoded feature sequences to extract common information, and a local attention unit is chosen to obtain the differentiated information, which is used as supplementary information. Finally, the accuracy, robustness, and computing time of the DSMTNet method are verified on experimental data. The results prove the superiority of the proposed method compared with other implemented approaches.

The Impact of Renewable Energy on Carbon Neutrality for the Sustainable Environment: Role of Green Finance and Technology Innovations

Over time environmental degradation has become a severe concern globally, especially in China. Therefore, to solve this serious issue, environmental economists have tried their best to explain the crux of environmental degradation. Besides such efforts, they have not reached a single opinion. Nowadays, economic progress has been considered a primary target to compete with other nations at any cost of environmental degradation. But, there may be some alternative solutions to reduce such costs, and the existing literature has not considered such core indicators. Thus, the present study assesses the eco-friendly variables for a sustainable environment in which green finance, green energy, and research and development expenditures. Moreover, this study also focuses on socio-economic factors like economic growth, urbanization, and openness. Time series estimators such as fully modified ordinary least square and Markov switching regression model are employed to investigate selected variables’ long-run impact on carbon emissions. The estimated outcomes show the significant contribution of green finance, green energy, openness, and R&D expenditures to environmental quality. Likewise, China’s urbanization and economic progress are harmful to environmental quality. Moreover, this study investigates the causal association between the selected variables and shows the two-way causal association between openness and emissions, RE and openness, and green finance and R&D expenditures. The results of the uni-directional association are also interesting. The current study develops some interesting policy implications for a sustainable environment on behalf of empirical outcomes.

Higher‐order asymptotics of minimax estimators for time series

We consider the minimax estimation of time series in view of higher‐order asymptotic theory. Under the framework of Bayesian inference, we focus on the Bayes estimator and the Bayesian Whittle estimator for parameter estimation. It is shown that these estimators are minimax with respect to the Bayes risk of higher‐order bias appeared in their asymptotic expansion. The minimax problem in the boundary issue with parameter on the boundary of parameter space is also discussed. Our theoretical discovery is justified by simulation studies even when the sample size is small.

Carbon dioxide intensity of GDP and environmental degradation in an emerging country.

This paper investigates whether the carbon dioxide (CO2) intensity of GDP matters for environmental degradation in an emerging country, namely, Turkey, over the period of 1990-2018 while controlling economic growth, foreign direct investment, and renewable energy consumption. The present study uses both linear and nonlinear time series estimators, namely, the Gregory and Hansen cointegration test, bounds test, nonlinear autoregressive distributed lag (NARDL) model, fully modified ordinary least squares (FMOLS), dynamic ordinary least squares (DOLS), and canonical cointegrating regressions (CCR), to encapsulate the possible effect of CO2 intensity of GDP, economic growth, foreign direct investment, and renewable energy consumption on environmentaldegradation in Turkey. The empirical finding of the present study reveals that the CO2 intensity of GDP is an important factor to determine environmental degradation in Turkey and the declining CO2 intensity of GDP reduces environmentaldegradation.Moreover, economic growth is the primary environmental sustainability factor in Turkey. The result is vital for policymaking and can perhaps be applied to take decisive policy actions to mitigate environmental issues.

Monitoring of Temperature Measurements for Different Flow Regimes in Water and Galinstan with Long Short-Term Memory Networks and Transfer Learning of Sensors

Temperature sensing is one of the most common measurements of a nuclear reactor monitoring system. The coolant fluid flow in a reactor core depends on the reactor power state. We investigated the monitoring and estimation of the thermocouple time series using machine learning for a range of flow regimes. Measurement data were obtained, in two separate experiments, in a flow loop filled with water and with liquid metal Galinstan. We developed long short-term memory (LSTM) recurrent neural networks (RNNs) for sensor predictions by training on the sensor’s own prior history, and transfer learning LSTM (TL-LSTM) by training on a correlated sensor’s prior history. Sensor cross-correlations were identified by calculating the Pearson correlation coefficient of the time series. The accuracy of LSTM and TL-LSTM predictions of temperature was studied as a function of Reynolds number (Re). The root-mean-square error (RMSE) for the test segment of time series of each sensor was shown to linearly increase with Re for both water and Galinstan fluids. Using linear correlations, we estimated the range of values of Re for which RMSE is smaller than the thermocouple measurement uncertainty. For both water and Galinstan fluids, we showed that both LSTM and TL-LSTM provide reliable estimations of temperature for typical flow regimes in a nuclear reactor. The LSTM runtime was shown to be substantially smaller than the data acquisition rate, which allows for performing estimation and validation of sensor measurements in real time.

Joint Autoencoder-Classifier Model for Malfunction Identification and Classification on Marine Diesel Engine Diagnostics Data

There has been an increasing demand on marine transportation and traveling, since the voyage of the ships are more economical and efficient than air or land-based alternatives. The propulsion of a ship is provided by a main engine system which includes the shaft, the propellers, and other auxiliary equipment. Marine diesel engine is a complex structure that the faults within these machines can cause malfunction of the whole system, which in turn inhibits the ship’s mission. It is crucial to monitor the engine and other auxiliary systems during the operation and infer their condition from their diagnostic data. In this study, we analyze monitoring data of a crude oil tanker for different ship loads and conditions. Our primary analysis includes main engine fault detection and classification for which we propose an end-to-end joint autoencoder-classifier model that contains a convolutional autoencoder, and a long-short term memory regressor connected to the latent space. Genetic algorithms optimized models gave us 93.61% accuracy for fault classification task. Further investigation on feature’s contributions to the model, we increased the accuracy up to 96%. One concern about marine transportation is the pollution of the air with greenhouse effect gases. In this study, we have developed NOx and SOx emission estimators for different faults and working conditions. Leveraging ship load, working conditions and engine faults in the models helped us to achieve 50% better estimation performance. Although there are other studies regarding gases emissions in the literature, this is the first study that took engine faults into account. We believe that the joint autoencoder-classifier model will be useful for other time series estimation task on other domains, especially where the operating condition plays a role in the process.

Satellite remote sensing of crop water use across the Missouri River Basin for 1986–2018 period

Understanding historical crop water use (CWU) dynamics is important to improve land and water management. In this study, well-validated (coefficient of determination = 0.91, percent bias = 4%, and percent root mean square error = 11.8%) Landsat-based actual evapotranspiration (ETa) time-series estimations were used to (1) assess summer season CWU (CWU-Su) dynamics, (2) investigate CWU-Su trends over the study period (1986–2018; 33 years) at the regional- and pixel-scales, and (3) attribute CWU-Su driving factors across Missouri River Basin. Spatial variability of the ETa estimations along with the observed bimodal probability density distribution of ETa highlighted a strong relation between land cover and water uses across the basin. The bimodal distribution of ETa also indicated the presence of two major landcovers in the basin. The drier foothill regions in northwestern Missouri River Basin, dominated by grassland/shrubland, showed lower ETa (< 500 mm/year), whereas cropland dominated regions in lower semi-humid basin and forested subbasins exhibited higher ETa (> 600 mm/year). The CWU-Su anomalies revealed the vulnerability of the basin to year-to-year weather conditions. The CWU-Su trend analysis revealed a significant positive trend (p < 0.1) at the regional-scale affecting 30% of basin’s cropland pixels. The cropland pixels under positive CWU-Su trend were found to be clustered in the eastern and central Missouri River Basin as a result of the combined effect of increased crop production area, increased crop yields, crop practice shifts to higher biomass crops, and increased irrigated land. The effect of improved irrigation and water management practices on reducing CWU-Su was observed in western Missouri River Basin, which had a stable major crop throughout the study period. Overall, the study highlights the usefulness of Landsat imagery and remote sensing-based ETa modeling approaches in generating historical time-series ETa maps over a wide range of elevation, vegetation, and climate.

Investigation of Long-Term Forest Dynamics in Protected Areas of Northeast China Using Landsat Data

Forest dynamics, including forest loss and gain, are long-term complex ecological processes affected by nature and human activities. It is particularly important to understand the long-term forest dynamics of protected areas to evaluate their conservation efforts. This study adopted the Landsat tree-canopy cover (TCC) method to derive annual TCC data for the period 1984–2020 for the protected areas of northeast China, where protection policies have been carried out since the end of the 20th century, e.g., the Natural Forest Conversion Program (NFCP). A strong correlation was found between the TCC estimates derived from Landsat and LiDAR observations, suggesting the high accuracy of TCC. Forest loss and gain events were also identified from the time series of TCC estimates. High correlations were reported for both forest loss (Producer’s accuracy = 85.21%; User’s accuracy = 84.26%) and gain (Producer’s accuracy = 87.74%; User’s accuracy = 88.31%), suggesting that the approach can be used for monitoring and evaluating the effectiveness of the NFCP and other forest conservation efforts. The results revealed a fluctuating upward trend of the TCC of the protected area from 1986 to 2018. The increased area of TCC was much larger than the decreased area, accounting for 59.68% and 40.34%, respectively, suggesting the effectiveness of protection policies. Since the NFCP was officially implemented in 1998, deforestation was effectively curbed, the area of forest loss was significantly reduced (slope: −14.24%/year), and the area of forest gain significantly increased (slope: 4.13%/year). We found that regional forest changes were mainly manifested in “forest gain after loss (forest recovery)” and “forest gain with no loss (forest newborn)”, accounting for 40.29% and 37.28% of the total area of forest change, respectively. Moreover, the forest gain area far exceeds the forest loss area, reaching 11.24 million hectares, suggesting a successful forest recovery due to forest protection.

A new approach towards temporal densification of river discharge estimates by filling cloud- and stripe-gaps using Landsat and Sentinel-2 images

ABSTRACT River discharge plays a vital role in the Earth’s water cycle. Recent studies have sought to estimate river discharge using optical sensors onboard satellites, typically deriving discharge from multispectral data after excluding many images that are contaminated by cloud or affected by the scan-line-corrector (SLC) failure on Landsat 7. As such, time series of estimated discharge generally include a high proportion of missing values. To overcome these issues, we propose an easy and feasible methodology to temporally densify the time series of discharge estimates by repairing cloud-contaminated and SLC-affected images on the Google Earth Engine platform. The middle Yangtze River (MYR) was chosen as the study area to demonstrate and evaluate the performance of the methodology. First, different water indices were compared to select the most appropriate indicator for water surface retrieval along the MYR. The Pekel method shows the best performance for extracting the water surface when turbid or under thin, semi-transparent clouds. Second, the performances of the Tourian method and the Manning equation for estimating the discharge were compared when using only good-quality images, yielding Nash-Sutcliffe efficiency (Nash) coefficients of 0.69 and 0.79, respectively. Images affected by the SLC and over 50% cloud cover accounted for 41% of 532 records used from 1986 to 2019. The repaired, temporally complete time series of discharge estimates by the Manning equation corresponds to a correlation coefficient of 0.86, a Nash coefficient of 0.73, and a root mean square error of 5697 m3/s from 2000 to 2019. These results show the strong potential of this method for estimating the discharge of rivers with known cross-sectional and the morphological conditions worldwide.

Using Support Vector Machine (SVM) with GPS Ionospheric TEC Estimations to Potentially Predict Earthquake Events

There are significant controversies surrounding the detection of precursors that may precede earthquakes. Natural hazard signatures associated with strong earthquakes can appear in the lithosphere, troposphere, and ionosphere, where current remote sensing technologies have become valuable tools for detecting and measuring early warning signals of stress build-up deep in the Earth’s crust (presumably associated with earthquake events). Here, we propose implementing a machine learning support vector machine (SVM) technique, applied with GPS ionospheric total electron content (TEC) pre-processed time series estimations, to evaluate potential precursors caused by earthquakes and manifested as disturbances in the TEC data. After filtering and screening our data for solar or geomagnetic influences at different time scales, our results indicate that for large earthquakes (&gt;Mw 6), true negative predictions can be achieved with 85.7% accuracy, and true positive predictions with an accuracy of 80%. We tested our method with different skill scores, such as accuracy (0.83), precision (0.85), recall (0.8), the Heidke skill score (0.66), and true skill statistics (0.66).

Foreign exchange market asymmetries in Pacific small island developing states: Evidence from Fiji

AbstractAfter abandoning Bretton Wood, the foreign exchange market has been dominated by three types of economies: export‐oriented economies (China and other Asian countries), commodity economies (Australia, New Zealand, Canada, and oil exporting nations) and reserve‐currency economies (US, EU, UK, and Swiss). As a result, the asymmetric development of the foreign exchange market has reduced the monetary and fiscal space for PSIDS, which face structural challenges such as a low population base, import dependence, aid dependency, climate risk, and political uncertainty. The ‘Exchange Market Pressure Index’ (EMPI) for Fiji is developed in this article to quantify the pressure on the exchange rate and monetary authorities' responses to micromanaging balance sheet impacts. The calculated EMPI accurately reflects four instances of financial distress in Fiji, including significant exchange market pressure in response to growing trade deficits and external debt, the global financial crisis's contagion effect, and political uncertainty. Our EMP Index's robustness is attributed in part to the employment of a dynamic time series estimate method, a time‐varying weighing scheme, and a high‐frequency monthly dataset.

Optimal difference-based variance estimators in time series: A general framework

Variance estimation is important for statistical inference. It becomes nontrivial when observations are masked by serial dependence structures and time-varying mean structures. Existing methods either ignore or sub-optimally handle these nuisance structures. This paper develops a general framework for the estimation of the long-run variance for time series with nonconstant means. The building blocks are difference statistics. The proposed class of estimators is general enough to cover many existing estimators. Necessary and sufficient conditions for consistency are investigated. The first asymptotically optimal estimator is derived. Our proposed estimator is theoretically proven to be invariant to arbitrary mean structures, which may include trends and a possibly divergent number of discontinuities.

Time-series estimation from randomly time-warped observations

• We consider the problem of estimating a signal given randomly time warped observations of the signal. • Many existing methods rely on estimating the unknown time warps, a computationally challenging problem. • We propose a scalable method based on interpreting the time warped observations as samples of a manifold. • We search for the ’center’ of the manifold, by resorting to techniques from graph/network processing. • We demonstrate that the number of time warped observations required is not exceedingly high for practical scenarios. We consider the problem of estimating a signal from its warped observations. Such estimation is commonly performed by altering the observations through some inverse-warping, or solving a computationally demanding optimization formulation . While these may be unavoidable if observations are few, when large amounts of warped observations are available, the cost of running such algorithms can be prohibitive. We consider the scenario where we have many observations, and propose a computationally simple algorithm for estimating the function of interest. We demonstrate the utility of the algorithm on streaming biomedical signals.

Pressure estimation of wave-in-deck loading using velocity fields obtained by particle image velocimetry

This study presents the application of a particle image velocimetry based pressure estimation method to reconstruct pressure distributions along a deck due to wave-in-deck loading. A series of experiments on wave-in-deck loading caused by focused waves were conducted in a two-dimensional wave tank to measure vertical force, pressure distributions and the PIV velocity fields under the deck. Fluid pressure fields were reconstructed by spatial integration of pressure gradients computed from Euler equation. The estimated pressures from PIV velocity data were compared and validated with the measured pressures by five pressure sensors under the deck. In the material acceleration calculation based on the Eulerian and pseudo-Lagrangian approaches, a proper time interval could be chosen to reduce errors in the pressure estimation results. The Eulerian approach provided less errors for pressure time series estimation, but the pseudo-Lagrangian approach showed a better agreement at maximum pressure. The estimated pressure fields and pressure distributions under the deck were studied to understand the mechanism of loading phenomena. Finally, the effect of the vortex beneath the leading edge during the water exit phase was discussed in relation to force and pressure measurements.

Adaptive Rejection of a Sinusoidal Disturbance with Unknown Frequency in a Flexible Rotor with Lubricated Journal Bearings

Frequency-dependent adaptive noise cancellation-based tracking controller (ANC-TC) is a known technique for the stabilization of several nonlinear dynamical systems. In recent years, this control strategy has been introduced and applied for the stabilization of a flexible rotor supported on full-lubricated journal bearings. This paper proposes a theoretical investigation, based on robust immersion and invariance (I&amp;I) theory, of a novel ANC-frequency estimation (FE) technique designed to stabilize a flexible rotor shaft affected by self-generated sinusoidal disturbances, generalized to the case of unknown frequency. A structural proof, under assumptions on closed-loop output signals, shows that the sinusoidal disturbance rejection is exponential. Numerical simulations are presented to validate the mathematical results in silico. The iterative Inexact Newton method is applied to the disturbance frequency and phase estimation error point series. The data fitting confirms that the phase estimation succession has an exponential convergence behavior and that the asymptotical frequency estimation is a warm-up phase of the overall close-loop disturbance estimation process. In two different operating conditions, the orders of convergence obtained by phase and frequency estimate timeseries are pφ=1, pω,unc=0.9983 and pω,cav=1.005. Rejection of the rotor dynamic disturbance occurs approximately 76% before in the cavitated than in the uncavitated condition, 2 (s) and 8.5 (s), respectively.

PPI and CPI seasonal adjustment during the COVID-19 pandemic

The U.S. Bureau of Labor Statistics publishes seasonally adjusted Consumer Price Index (CPI) and Producer Price Index (PPI) data monthly. Seasonal adjustment removes within-year seasonal patterns from data. To seasonally adjust data and estimate seasonal patterns of time series, the CPI and PPI use a filter-based approach that employs moving averages of historical data. In 2020, many PPIs and CPIs experienced extreme movements because of the coronavirus disease 2019 (COVID-19) pandemic. For example, the PPI and CPI for gasoline decreased 53.0 percent and 16.5 percent in April 2020, respectively. Because the CPI and PPI use historical data to estimate seasonal patterns, the extreme price movements in 2020 could have adversely affected the capability of the two price programs to accurately estimate seasonally adjusted data. This article explains how the CPI and PPI mitigated the effects of the COVID-19 pandemic on their seasonally adjusted price indexes. Mitigation steps included identifying price indexes whose movements were affected by the pandemic, estimating time series models to quantify these effects, and removing pandemic-related price movements from the data before estimating seasonal patterns.