Search Results (1,122)

Wholesale sales value is one of the key elements included in the coincident indicator series of the indexes of business conditions in Japan. The objectives of this study are twofold. The first is to comprehend features of dynamic structure of various components for 12 business types of the wholesale sales in Japan, focusing on the period from January 1980 to December 2022. The second is to elucidate effect of business cycles on the behavior of each business type of wholesale sales. Specifically, we utilize our moving linear model approach to decompose monthly time-series data of wholesale sales into a seasonal component, an unusually varying component containing outliers, a constrained component, and a remaining component. Additionally, we construct a distribution-free dynamic linear model and examine the time-varying relationship between the decomposed remaining component, which contains cyclical variation, in each business type of the wholesale sales and that in the coincident composite index. Our proposed approach reveals complex dynamics of various components of time series on wholesale sales. Furthermore, we find that different business types of the wholesale sales exhibit diverse responses to business cycles, which are influenced by macroeconomic conditions, government policies, or exogenous shocks. Full article

The relationship between the seismic regime and humidity, pressure, temperature, and wind speed measured at a network of stations on the Japanese islands was studied for the period from 1973 to 2025. For each of the parameters, weighted average time series were constructed using the principal component method and then subjected to wavelet decomposition. For wavelet decomposition levels, the amplitudes of the envelopes and the points of their local extrema were found and compared with the times at which earthquakes occurred. The problem of estimating an advanced measure of envelope extremum points relative to earthquake moments was considered using a model of interacting point processes. For a sequence of 213 strong earthquakes with a magnitude of at least 6.5, the same numbers for the largest local maxima and the smallest local minima were selected for the extrema of the envelope amplitude of each parameter. It turned out that the largest advance measures occurred for the seventh level of decomposition (the period from 16 to 32 days). Two advance mechanisms were identified: one mechanism is associated with the trigger effect of cyclones on seismicity, and the second is associated with the occurrence of atmospheric earthquake precursors. Full article

To address the challenge of effectively filtering out noise components in GPS coordinate time series, we propose a denoising method based on parameter-optimized variational mode decomposition (VMD). The method combines permutation entropy with mutual information as the fitness function and uses the crayfish (COA) algorithm to adaptively obtain the optimal parameter combination of the number of modal decompositions and quadratic penalty factors for VMD, and then, sample entropy is used to identify effective mode components (IMF), which are reconstructed into denoised signals to achieve effective separation of signal and noise The experiments were conducted using simulated signals and 52 GPS station data from CMONOC to compare and analyze the COA–VMPE–WD method with wavelet denoising (WD), empirical mode decomposition (EMD), ensemble empirical mode decomposition (EEMD), and complete ensemble empirical mode decomposition with adaptive noise (CEEMDAN) methods. The result shows that the COA–VMPE–WD method can effectively remove noise from GNSS coordinate time series and preserve the original features of the signal, with the most significant effect on the U component. The COA–VMPE–WD method reduced station velocity by an average of 50.00%, 59.09%, 18.18%, and 64.00% compared to the WD, EMD, EEMD, and CEEMDAN methods. The noise reduction effect is higher than the other four methods, providing reliable data for subsequent analysis and processing. Full article

Predicting building energy consumption is an essential part of demand-side management since it enables cost-effective building operation under limited resources. Recent prediction models have adopted deep learning networks due to their high capabilities in extracting occupants’ energy use patterns from historical data. Additionally, augmenting historical data by decomposing existing input times-series into several temporal components can enhance prediction performance, particularly for new buildings. However, it still remains unclear how newly created predictors, through the decomposition of existing time-series, affect the performance of building energy use prediction. Therefore, to address this gap, this study proposed a deep learning-based energy use prediction framework that employs the Unobserved Component Model to create new input time-series based on existing ones. Then, the performance of the proposed prediction framework was evaluated using two years of historical data collected from a case building. The main findings are threefold. First, deep learning networks achieved a higher prediction performance during training than during testing. Second, testing performance was generally better when using the augmented dataset than the raw dataset. Third, the proposed data augmentation method contributes to a 1.26% decrease in MAPE and a 3.42% decrease in CvRMSE. This suggests that the proposed prediction framework can be applied to simulations of buildings with limited time series dataset to more accurately predict energy consumption at the building level. Full article

The On-Load Tap Changer (OLTC), as a critical component of transformers, undergoes frequent switching operations that can lead to faults such as contact wear and arc discharge, which are often difficult to detect at an early stage using traditional monitoring methods. In particular, dissolved gas analysis (DGA) in OLTC oil is challenged by the unique oil gas decomposition mechanisms and the presence of background noise, making conventional DGA criteria less effective. Moreover, OLTC oil monitoring data are typically obtained through intermittent sampling, resulting in sparse time series with low resolution that complicate fault prediction. To address these challenges, this paper proposes an integrated framework combining LGOD-based anomaly detection, Locally Weighted Regression (LWR) for data repair, and the ETSformer temporal prediction model. This approach effectively identifies and corrects anomalies, restores the dynamic variation trends of gas concentrations, and enhances prediction accuracy through deep temporal modeling, thereby providing more reliable data support for OLTC state assessment and fault diagnosis. Experimental results demonstrate that the proposed method significantly improves prediction accuracy, enhances sensitivity to gas concentration evolution, and exhibits robust adaptability under both normal and fault scenarios. Furthermore, ablation experiments confirm that the observed performance gains are attributable to the complementary contributions of LGOD, LWR, and ETSformer, rather than any single component alone, highlighting the effectiveness of the integrated approach. Full article

Accurate load forecasting for community-level heating, ventilation, and air conditioning (HVAC) plays an important role in determining an efficient strategy for demand response (DR) and the operation of the power grid. However, community-level HVAC includes various building-level HVACs, whose usage patterns and standard parameters vary, causing the challenge of load forecasting. To this end, a novel deep learning model, multiscale and cross-variable transformer (MSCVFormer), is proposed to achieve accurate community-level HVAC probabilistic load forecasting by capturing the various influences of multivariables on the load pattern, providing effective information for the grid operators to develop DR and operation strategies. This approach is combined with the multiscale attention (MSA) and cross-variable attention (CVA) mechanism, capturing the complex temporal patterns of the aggregated load. Specifically, by embedding the time series decomposition into the self-attention mechanism, MSA enables the model to capture the critical features of time series while considering the correlation between multiscale time series. Then, CVA calculates the correlations between the exogenous variable and aggregated load, explicitly utilizing the exogenous variables to enhance the model’s understanding of the temporal pattern. This differs from the usual methods, which do not fully consider the relationship between the exogenous variable and aggregated load. To test the effectiveness of the proposed method, two datasets from Germany and China are used to conduct the experiment. Compared to the benchmarks, the proposed method achieves outperforming probabilistic load forecasting results, where the prediction interval coverage probability (PICP) deviation with the nominal coverage and prediction interval normalized averaged width (PINAW) are reduced by 46.7% and 5.25%, respectively. Full article

Analysis of global mean sea-level (GMSL) variations provides insights into their spatial and temporal characteristics. To analyze the sea-level cycle and its correlation with the El Niño–Southern Oscillation (ENSO, represented by the Oceanic Niño Index), this study proposes an enhanced analytical framework integrating Local Mean Decomposition with an improved wavelet thresholding technique and wavelet transform. The GMSL time series (January 1993 to July 2020) underwent multi-scale decomposition and noise reduction using Local Mean Decomposition combined with improved wavelet thresholding. Subsequently, the Morlet continuous wavelet transform was applied to analyze the signal characteristics of both GMSL and the Oceanic Niño Index. Finally, cross-wavelet transform and wavelet coherence analyses were employed to investigate their correlation and phase relationships. Key findings include the following: (1) Persistent intra-annual variability (8–16-month cycles) dominates the GMSL signal, superimposed by interannual fluctuations (4–8-month cycles) related to climatic and seasonal forcing. (2) Phase analysis reveals that GMSL generally leads the Oceanic Niño Index during El Niño events but lags during La Niña events. (3) Strong El Niño episodes (May 1997 to May 1998 and October 2014 to April 2016) resulted in substantial net GMSL increases (+7 mm and +6 mm) and significant peak anomalies (+8 mm and +10 mm). (4) Pronounced negative peak anomalies occur during La Niña events, though prolonged events are often masked by the long-term sea-level rise trend, whereas shorter events exhibit clearly discernible and rapid GMSL decline. The results demonstrate that the proposed framework effectively elucidates the multi-scale coupling between ENSO and sea-level variations, underscoring its value for refining the understanding and prediction of climate-driven sea-level changes. Full article

Hydrological runoff prediction serves as the core technological foundation for water resource management and flood/drought mitigation. However, the nonlinear, non-stationary, and multi-temporal scale characteristics of runoff data result in insufficient accuracy of traditional prediction methods. To address the challenges of single decomposition methods’ inability to effectively separate multi-scale components and single deep learning models’ limitations in capturing long-range dependencies or extracting local features, this study proposes an Informer-GRU runoff prediction model based on STL-CEEMDAN secondary decomposition and Gorilla Troops Optimizer (GTO). The model extracts trend, seasonal, and residual components through STL decomposition, then performs fine decomposition of the residual components using CEEMDAN to achieve effective separation of multi-scale features. By combining Informer’s ProbSparse attention mechanism with GRU’s temporal memory capability, the model captures both global dependencies and local features. GTO is introduced to optimize model architecture and training hyperparameters, while a multi-objective loss function is designed to ensure the physical reasonableness of predictions. Using daily runoff data from the Liyuan Basin in Yunnan Province (2015–2023) as a case study, the results show that the model achieves a coefficient of determination (R²) and Nash-Sutcliffe efficiency coefficient (NSE) of 0.9469 on the test set, with a Kling-Gupta efficiency coefficient (KGE) of 0.9582, significantly outperforming comparison models such as LSTM, GRU, and Transformer. Ablation experiments demonstrate that components such as STL-CEEMDAN secondary decomposition and GTO optimization enhance model performance by 31.72% compared to the baseline. SHAP analysis reveals that seasonal components and local precipitation station data are the core driving factors for prediction. This study demonstrates exceptional performance in practical applications within the Liyuan Basin, providing valuable insights for water resource management and prediction research in this region. Full article

To address the limitations of the traditional methods that are used to extract features from non-stationary signals and capture temporal dependency relationships, a rolling bearing fault diagnosis method combining variational mode decomposition (VMD) and deep learning is proposed. A hybrid VMD-CNN-Transformer model is constructed, where VMD is used to adaptively decompose bearing vibration signals into multiple intrinsic mode functions (IMFs). The convolutional neural network (CNN) captures the local features of each modal time series, while the multi-head self-attention mechanism of the Transformer captures the global dependencies of each mode, enabling the global analysis and fusion of features from each mode. Finally, a fully connected layer is used to classify the 10 fault types. The experimental results on the Case Western Reserve University bearing dataset demonstrate that the model achieves a fault diagnosis accuracy of 99.48%, which is significantly higher than that of single or traditional combined methods, providing a new technical path for the intelligent diagnosis of rolling bearing faults. Full article

Accurate prediction of the air quality index (AQI) is essential for environmental monitoring and sustainable urban planning. With rising pollution from industrialization and urbanization, particularly from fine particulate matter (PM_2.5, PM₁₀), nitrogen dioxide (NO₂), and ozone (O₃), robust forecasting tools are needed to support timely public health interventions. This study proposes a hybrid deep learning framework that combines empirical mode decomposition (EMD) and ensemble empirical mode decomposition (EEMD) with two recurrent neural network architectures: long short-term memory (LSTM) and gated recurrent unit (GRU). A comprehensive dataset from Xitun District, Taichung City—including AQI and 18 pollutant and meteorological variables—was used to train and evaluate the models. Model performance was assessed using root mean square error, mean absolute error, mean absolute percentage error, and the coefficient of determination. Both LSTM and GRU models effectively capture the temporal patterns of air quality data, outperforming traditional methods. Among all configurations, the EEMD-GRU model delivered the highest prediction accuracy, demonstrating strong capability in modeling high-dimensional and nonlinear environmental data. Furthermore, the incorporation of decomposition techniques significantly reduced prediction error across all models. These findings highlight the effectiveness of hybrid deep learning approaches for modeling complex environmental time series. The results further demonstrate their practical value in air quality management and early-warning systems. Full article

One of the important techniques for solving several partial differential equations is the residual power series method, which provides the approximate solutions of differential equations in power series form.In this work, we use Aboodh transform in the analogical structure of the residual power series method to obtain a new method called the Aboodh residual power series method (ARPSM). By using this technique, we calculate the coefficients of some power series solutions of time Caputo fractional Kawahara equations. To obtain analytical and numerical solutions for the TCFKEs, we use ARPSM, first with the approximate initial condition and then with the exact initial condition. We present ARPSM’s reliability, efficiency, and capability by graphically describing the numerical results for analytical solutions and by comparing our solutions with other solutions for the TCFKEs obtained using two alternative methods, namely, the residual power series method and the natural transform decomposition method. Full article

Temporal and spatial variations in water temperature were analyzed from 1998 to 2023 across two contrasting southeast Basque coast estuaries: the deeper, stratified estuary of Bilbao and the shallower, mixed estuary of Urdaibai. We assessed long-term trends, seasonality, intra- and inter-estuary differences, and links to hydro-meteorological drivers using time-series decomposition, clustering, cumulative sum, regression, and correlation analyses. The largest differences in interannual and seasonal patterns occurred between outer neritic and shallow transitional waters. Most water masses warmed overall, with increases until 2003–2006, followed by cooling until 2013–2015, and sharp warming in 2020–2023. The strongest trends (0.24–0.25 °C decade⁻¹) occurred in middle-estuary waters, while inner above-halocline waters of the stratified estuary showed no trend or slight cooling. The strongest warming occurred in spring, particularly in the easternmost mixed estuary (0.49 °C decade⁻¹), especially in May (0.88 °C decade⁻¹). Seasonal minima and maxima occurred earlier in surface transitional waters than in neritic and deep transitional waters of the stratified system. Over time, temperature maxima advanced, minima were delayed, shortening the warming phase, and springs became warmer, extending the warm season. Air temperature was the main driver of water temperature trends, while river flow modulated patterns at annual and seasonal scales, with negative correlations with temperature, mainly in spring. Full article

Accurate prediction of river runoff is significant for flood control, water resource allocation, and basin ecological management. Despite the promise of integrating signal decomposition with deep learning, current decomposition-based hybrid models face critical forward data contamination: decomposition algorithms improperly access future test data in full-series applications, artificially inflating prediction accuracy. In contrast, the stepwise decomposition method currently proposed leads to high computational costs. To address this limitation, we introduce a novel framework integrating segmented decomposition sampling with a multi-input neural network. Specifically, a hybrid forecasting model combining Seasonal-Trend decomposition using Loess (STL) and Convolutional Long Short-Term Memory (CNN-LSTM) networks was implemented for daily runoff estimation. Method reliability was evaluated using historical runoff data from Huaxian Station in China’s Weihe River Basin, with comparative experiments conducted against established single and hybrid models. The results showed that the proposed framework can effectively avoid future information leakage and simultaneously improve prediction accuracy. For 1–3-day-ahead Nash-Sutcliffe efficiency (NSE) at Huaxian Station, the STL-CNN-LSTM model achieved values of 0.96, 0.83, and 0.80, respectively—representing improvements of 5.49%, 5.06%, and 12.68% over the VMD-CNN-LSTM model. This STL-based configuration outperformed the standalone LSTM counterpart by 23.08%, 9.21%, and 17.65% in NSE, respectively. Therefore, the proposed framework, which incorporates the segmented decomposition sampling method and a multi-input neural network, proves to be both practical and reliable. Full article

As the world’s most populous and geographically diverse continent, active fire occurrence in Asia exhibits pronounced spatiotemporal heterogeneity, driven by climactic and anthropogenic factors. However, systematic analyses of Asian fire occurrence characteristics are still scarce, the quantitative and spatial relationship between fire dynamics and drivers remain poorly understood. Here, utilizing active fire and land cover products alongside climate and human footprint datasets, we explored the spatiotemporal distribution and dynamics of active fire counts (FC) over 20 years (2003–2022) in Asia, quantifying the effects of climate and human management. Results analyzed over 10 million active fires, with cropland fires predominating (25.6%) and Southeast Asia identified as the hotspot. FC seasonal dynamics were governed by temperature and precipitation, while spring was the primary burning season. A continental inter-annual FC decline (mean slope: −8716 yr⁻¹) was identified, primarily attributed to forest fire reduction. Subsequently, we further clarified the drivers of FC dynamics. Time series decomposition attributed short-term FC fluctuations to extreme climate events (e.g., 2015 El Niño), while long-term trends reflected cumulative human interventions (e.g., cropland management). The trend analysis revealed that woody vegetation fires in the Indochina Peninsula shifted to herbaceous fires, Asian cropland FC primarily increased but were restricted in eastern China and Thailand by strict policies. Spatially, hydrometeorological factors dominated 58.1% of FC variations but exhibited opposite effects between arid and humid regions, followed by human factor, where human activities shifted from fire promotion to suppression through land-use transitions. These driving mechanism insights establish a new framework for adaptive fire management amid escalating environmental change. Full article

In the weld bonding used in automobiles, inspecting the adhesive areas is important to achieve the desired increase in rigidity. Active infrared thermography using flash lamp heating was applied to a weld-bonded specimen. Temperature differences were observed on the measurement surface depending on the presence or absence of adhesive, enabling the detection of the bonded areas. Furthermore, singular value decomposition (SVD) was applied to obtain time-series temperature variation data. SVD emphasizes the boundaries of the adhesive areas, improving the accuracy of inspections of the adhesive application areas. Full article