Components Of Time Series Research Articles

The use of Principal Component Regression and Time Series Analysis to Predict Nitrous Oxide Emissions in Ghana

The disturbing pace of emanation of Nitrous Oxide (N2O) into the atmosphere and its calamitous impact on the environment, as monitored by many governmental agencies and researchers has become a wellspring of worry for many nations and therefore needs due attention. The study deployed nitrous oxide emissions from the three sectors to the total nitrous oxide emissions in Ghana over the period 1990 to 2016. The sectors, energy sector, Agriculture Forestry and Other Land Use (AFOLU) and Waste sector were considered against the total N2O emissions. Principal Component Regression (PCR) was applied to the input variables for the reduction of its large size to a few principal components to explain the variations in the original dataset since there was the presence of multicollinearity. Autoregressive Integrated Moving Average (ARIMA) was used to develop models to predict the total N2O emissions and emissions from the sectors in Ghana. The appropriate models that fitted the data well were ARIMA (1,2,1) and ARIMA (1,1,2) based on information criteria (AIC, AICc and BIC). The ARIMA (1,2,1) model was found to be the most suitable model for predicting N2O emission from Energy sector and Waste sector. 70% Of the dataset was used for the analysis and the results from the forecasted values mimic the original dataset. It was revealed that the AFOLU sector is the predominant sector that significantly contribute the overall N2O emission in the atmosphere based on standardized coefficient. The model was adequate since its MAPE for AFOLU sector and the total N2O emissions were 2.95 and 2.68% respectively, meaning the model explained 97.05 and 97.32% respectively. The predicted values mimic the trend of the current situation at hand.

Least-squares Wavelet and Cross-wavelet Analyses of VLBI Baseline Length and Temperature Time Series: Fortaleza–Hartebeesthoek–Westford–Wettzell

Extensive least-squares wavelet and cross-wavelet analyses are performed on the Very Long Baseline Interferometry (VLBI) baseline length and temperature time series for a network of four VLBI antennas located in different continents. These analyses do not rely on any pre-processing of the measurements including interpolations or gap-filling, and they can provide accurate instantaneous frequency information along with phase differences in the time–frequency domain. Out of four antennas mounted in Fortaleza, Hartebeesthoek, Westford, and Wettzell, the most and the least possibly impacted VLBI time series by the annual temperature variation are Westford–Wettzell and Fortaleza–Hartebeesthoek, respectively. Furthermore, the annual components of the temperature time series in Westford and Wettzell lead the ones in the corresponding VLBI time series by nearly one month, where one of the baseline sides is either in Westford or Wettzell.

Change Detection within Remotely Sensed Satellite Image Time Series via Spectral Analysis

Jump or break detection within a non-stationary time series is a crucial and challenging problem in a broad range of applications including environmental monitoring. Remotely sensed time series are not only non-stationary and unequally spaced (irregularly sampled) but also noisy due to atmospheric effects, such as clouds, haze, and smoke. To address this challenge, a robust method of jump detection is proposed based on the Anti-Leakage Least-Squares Spectral Analysis (ALLSSA) along with an appropriate temporal segmentation. This method, namely, Jumps Upon Spectrum and Trend (JUST), can simultaneously search for trends and statistically significant spectral components of each time series segment to identify the potential jumps by considering appropriate weights associated with the time series. JUST is successfully applied to simulated vegetation time series with varying jump location and magnitude, the number of observations, seasonal component, and noises. Using a collection of simulated and real-world vegetation time series in southeastern Australia, it is shown that JUST performs better than Breaks For Additive Seasonal and Trend (BFAST) in identifying jumps within the trend component of time series with various types. Furthermore, JUST is applied to Landsat 8 composites for a forested region in California, U.S., to show its potential in characterizing spatial and temporal changes in a forested landscape. Therefore, JUST is recommended as a robust and alternative change detection method which can consider the observational uncertainties and does not require any interpolations and/or gap fillings.

Solar Activity Predictability Horizons

The chaotic component in solar-activity variations, the limited amount of observational statistics, and the use of proxy data impose limitations on long-term predictions. This study uses the well-known reconstructions of total solar irradiance (TSI) from optical observations (from 1610 AD) and cosmogenic radioisotopes (from 7362 BC) to estimate the maximum Lyapunov exponent for 11-year and secular cycles. This is done with the classical algorithm described by Wolf et al. (1985). The maximum Lyapunov exponents turned out to be positive in both cases, thus confirming the presence of the chaotic component in time series. We then estimate the predictability horizon Tp with the well-known error for each reconstruction. It turned out that the predictability horizon is on average 10 ≤ Tp ≤ 22 years for 11-year cycles and on average 40 ≤ Tp ≤ 60 years for secular cycles.

Altered between-network connectivity in individuals prone to obesity

ObjectiveInvestigating intrinsic brain functional connectivity may help identify the neurobiology underlying cognitive patterns and biases contributing to obesity propensity. To address this, the current study used a novel whole-brain, data-driven approach to examine functional connectivity differences in large-scale network interactions between obesity-prone (OP) and obesity-resistant (OR) individuals. MethodsOR (N = 24) and OP (N = 25) adults completed functional magnetic resonance imaging (fMRI) during rest. Large-scale brain networks were identified using independent component analysis (ICA). Voxel-specific between-network connectivity analysis assessed correlations between ICA component time series’ and individual voxel time series, identifying regions strongly connected to many networks, i.e., “hubs”. ResultsSignificant group differences in between-network connectivity (OP vs. OR; FDR-corrected) were observed in bilateral basal ganglia (left: q = 0.009; right: q = 0.010) and right dorsolateral prefrontal cortex (dlPFC; q = 0.026), with OP>OR. Basal ganglia differences were largely driven by a more strongly negative correlation with a lateral sensorimotor network in OP, with dlPFC differences driven by a more strongly negative correlation with an inferior visual network in OP. ConclusionsGreater between-network connectivity was observed in the basal ganglia and dlPFC in OP, driven by stronger associations with lateral sensorimotor and inferior visual networks, respectively. This may reflect a disrupted balance between goal-directed and habitual control systems and between internal/external monitoring processes.

Study of time series of meteorological parameters by wavelet analysis

The paper presents the results of applying the continuous wavelet analysis method to assess the cyclic components of discrete time series. The work is based on an analysis of a long-term series of data on observations of air temperature in the south-west of the Valdai Upland, as well as analysis of data on observations of CO2 concentration in the canopy of the tropical monsoon forest in southern Vietnam. The patterns of wavelet coefficients and integral spectra have been studied for all considered time series. Additionally, the cyclicities contained in the studied series were revealed.

Analysis on the Characteristics of Dry and Wet Periods in The Yangtze River Basin

As China’s main grain producing region, the Yangtze River basin is vulnerable to changes in wet and dry conditions. In this study, the monthly scale of standardized precipitation evapotranspiration index (SPEI) was calculated, based on the Penman–Monteith equation from 239 meteorological stations in the Yangtze River basin, from 1960 to 2017. Water regime characteristic areas of the Yangtze River basin were extracted and divided using the rotating empirical orthogonal function (REOF). The linear trend of the drought and wetness indicators, the abrupt changes of the rotated principal component time series (RPCs), and the change periods of the drought/wetness intensity (DI/WI) in each subregion were analyzed and discussed. Subsequently, the effects of El Niño-southern oscillation (ENSO) and arctic oscillation (AO) on drought and wetness events were discussed. The results showed that the Yangtze River basin has the characteristic of coexistence of drought and wetness, and drought and wetness of similar severity tend to occur in the same region. There were six subregions extracted through REOF, based on the monthly scale of SPEI, of which the northwestern pattern had an aridization tendency. The stations with significantly increased wetness were located in the middle and eastern basin. The stations in the south of the northwestern pattern, and the west of the southern pattern, had a tendency of wetting in the first 29 years, however, there has been a significant tendency of drying in this region in the last 29 years, which was caused by an abrupt change in 1994. In addition, other patterns had multiple abrupt changes, resulting in multiple transitions between dry and wet states. The principal periods of WI in the southern pattern and northern pattern were longer than the DI, but in other subregions DI was longer than WI. ENSO and AO had the most obvious influence on DI and WI. Compared with the cold phase of ENSO, the DI/WI in the warm phase were higher/lower; compared with the negative phase of AO, both DI and WI were higher in the positive phase. The Hurst index showed that the current dry and wet conditions in the Yangtze River basin have persistent characteristics, the dry conditions in each subregion will continue in the future, and there were a few wetness indicators with weak anti-persistence.

Time series and fractal analyses of wheezing: a novel approach.

Since the outbreak of the pandemic Coronavirus Disease 2019, the world is in search of novel non-invasive methods for safer and early detection of lung diseases. The pulmonary pathological symptoms reflected through the lung sound opens a possibility of detection through auscultation and of employing spectral, fractal, nonlinear time series and principal component analyses. Thirty-five signals of vesicular and expiratory wheezing breath sound, subjected to spectral analyses shows a clear distinction in terms of time duration, intensity, and the number of frequency components. An investigation of the dynamics of air molecules during respiration using phase portrait, Lyapunov exponent, sample entropy, fractal dimension, and Hurst exponent helps in understanding the degree of complexity arising due to the presence of mucus secretions and constrictions in the respiratory airways. The feature extraction of the power spectral density data and the application of principal component analysis helps in distinguishing vesicular and expiratory wheezing and thereby, giving a ray of hope in accomplishing an early detection of pulmonary diseases through sound signal analysis.

Forecasting the Yellow River runoff based on functional data analysis methods

This study examines the runoff prediction of each hydrometric station and each month in the mainstream of the Yellow River in China. From the perspective of functional data, the monthly runoff of each hydrometric station can be regarded as a function of both time and space. A sequence of such functions is formed by collecting the data over the years. We propose a new approach by combining the two-dimensional functional principal component analysis (FPCA) and time series analysis methods to predict the runoff. In the simulation, we compared the proposed method with two others: one based on one-dimensional FPCA and the seasonal auto-regressive integrated moving average (SARIMA) method. The method combining standard two-dimensional FPCA and time series analysis outperforms others in most cases, and is used to predict the runoff of each hydrometric station and each month in the Yellow River in 2018.

A Novel Deep Learning Approach for Wind Power Forecasting Based on WD-LSTM Model

Wind power generation is one of the renewable energy generation methods which maintains good momentum of development at present. However, its extremely intense intermittences and uncertainties bring great challenges to wind power integration and the stable operation of wind power grids. To achieve accurate prediction of wind power generation in China, a hybrid prediction model based on the combination of Wavelet Decomposition (WD) and Long Short-Term Memory neural network (LSTM) is constructed. Firstly, the nonstationary time series is decomposed into multidimensional components by WD, which can effectively reduce the volatility of the original time series and make them more stable and predictable. Then, the components of the original time series after WD are used as input variables of LSTM to predict the national wind power generation. Forty points were used, 80% as training samples and 20% as testing samples. The experimental results show that the MAPE of WD-LSTM is 5.831, performing better than other models in predicting wind power generation in China. In addition, the WD-LSTM model was used to predict the wind power generation in China under different development trends in the next two years.

A time series model based on hybrid-kernel least-squares support vector machine for short-term wind power forecasting

In this paper, a time series model based on hybrid-kernel least-squares support vector machine (HKLSSVM) with three processes of decomposition, classification, and reconstruction is proposed to predict short-term wind power. Firstly, on the basis of the maximal wavelet decomposition (MWD) and fuzzy C-means algorithm, a decomposition method decomposes wind power time series and classifies the decomposition time series components into three classes according to amplitude–frequency characteristics. Then, time series models on the basis of least-squares support vector machine (LSSVM) with three different kernels are established for these three classes. Non-dominated sorting genetic algorithm II optimizes the parameters of each forecasting model. Finally, outputs of forecasting models are reconstructed to obtain the forecasting power. The proposed model is compared with the empirical-mode-decomposition least-squares support vector machine (EMD-LSSVM) model and wavelet-decomposition least-squares support vector machine (WDLSSVM) model. The results of the comparison show that proposed model performs better than these benchmark models.

Interannual variations in the amplitude of 25–70-day intraseasonal oscillations in Central Africa and relationship with ENSO

The interannual variations in the intraseasonal oscillations (ISO) activity have long been investigated in different geographical areas around the world. However, Central Africa (CA) remains a serious gap in climate variability studies, especially at intraseasonal timescales because very few of these studies focused on this region. In this paper, we investigated the interannual variations in the amplitude of intraseasonal oscillations (ISO) in CA, using an ISO index (ISOI), extracted from the wavelet transform on the 25–70-day filtered daily Outgoing Longwave Radiation (OLR) data. Results showed that the ISOI in CA undergoes large interannual variations with some years of high as 1985 and 1990, and years of very low ISO as 1981 and 2001. The composite analysis revealed that the warm El Nino Southern Oscillation (ENSO) years are characterized by negative ISOI in most part of CA, while cold ENSO years are dominated by positive ISOIs. The lagged correlations computed between ISOIs and Nino3.4 index showed that the relationship ISOI-ENSO varies much spatially, but the correlation coefficients are globally low, with maxima around ± 0.5. The empirical orthogonal function (EOF) analysis applied on monthly ISOI revealed that two main spatial structures characterize ISOI variability, which explain 15.7% and 8.4% of the total variance, respectively. The first is marked by mostly positive loadings, crossing CA from northwest to southeast, and the second emerges as a quasi-meridional, with positive loadings located around the latitude equator. The principal component (PC) time series confirm the strong interannual variations of the ISOI amplitude. The extreme ENSO years are characterized by an anomalous easterly winds coming from the Indian Ocean, which reverses around Eastern Central Africa borders and blow back into the ocean. These easterly winds bring warm (or cold) air fluxes from the Ocean, which may be responsible for the modulation of the atmospheric anomalous convection at intraseasonal timescales.

Nonparametric trend estimation in functional time series with application to annual mortality rates.

We address the problem of trend estimation for functional time series. Existing contributions either deal with detecting a functional trend or assuming a simple model. They consider neither the estimation of a general functional trend nor the analysis of functional time series with a functional trend component. Similarly to univariate time series, we propose an alternative methodology to analyze functional time series, taking into account a functional trend component. We propose to estimate the functional trend by using a tensor product surface that is easy to implement, to interpret, and allows to control the smoothness properties of the estimator. Through a Monte Carlo study, we simulate different scenarios of functional processes to show that our estimator accurately identifies the functional trend component. We also show that the dependency structure of the estimated stationary time series component is not significantly affected by the error approximation of the functional trend component. We apply our methodology to annual mortality rates inFrance.

Comparison of Harmonic Analysis of Time Series (HANTS) and Multi-Singular Spectrum Analysis (M-SSA) in Reconstruction of Long-Gap Missing Data in NDVI Time Series

Monitoring vegetation changes over time is very important in dry areas such as Iran, given its pronounced drought-prone agricultural system. Vegetation indices derived from remotely sensed satellite imageries are successfully used to monitor vegetation changes at various scales. Atmospheric dust as well as airborne particles, particularly gases and clouds, significantly affect the reflection of energy from the surface, especially in visible, short and infrared wavelengths. This results in imageries with missing data (gaps) and outliers while vegetation change analysis requires integrated and complete time series data. This study investigated the performance of HANTS (Harmonic ANalysis of Time Series) algorithm and (M)-SSA ((Multi-channel) Singular Spectrum Analysis) algorithm in reconstruction of wide-gap of missing data. The time series of Normalized Difference Vegetation Index (NDVI) retrieved from Landsat TM in combination with 250m MODIS NDVI time image products are used to simulate and find periodic components of the NDVI time series from 1986 to 2000 and from 2000 to 2015, respectively. This paper presents the evaluation of the performance of gap filling capability of HANTS and M-SSA by filling artificially created gaps in data using Landsat and MODIS data. The results showed that the RMSEs (Root Mean Square Errors) between the original and reconstructed data in HANTS and M-SSA algorithms were 0.027 and 0.023 NDVI value, respectively. Further, RMSEs among 15 NDVI images extracted from the time series artificially and reconstructed by HANTS and M-SSA algorithms were 0.030 and 0.025 NDVI value, respectively. RMSEs of the original and reconstructed data in HANTS and M-SSA algorithms were 0.10 and 0.04 for time series 6, respectively. The findings of this study present a favorable option for solving the missing data challenge in NDVI time series.

Nonlinear time series and principal component analyses: Potential diagnostic tools for COVID-19 auscultation.

The development of novel digital auscultation techniques has become highly significant in the context of the outburst of the pandemic COVID 19. The present work reports the spectral, nonlinear time series, fractal, and complexity analysis of vesicular (VB) and bronchial (BB) breath signals. The analysis is carried out with 37 breath sound signals. The spectral analysis brings out the signatures of VB and BB through the power spectral density plot and wavelet scalogram. The dynamics of airflow through the respiratory tract during VB and BB are investigated using the nonlinear time series and complexity analyses in terms of the phase portrait, fractal dimension, Hurst exponent, and sample entropy. The higher degree of chaoticity in BB relative to VB is unwrapped through the maximal Lyapunov exponent. The principal component analysis helps in classifying VB and BB sound signals through the feature extraction from the power spectral density data. The method proposed in the present work is simple, cost-effective, and sensitive, with a far-reaching potential of addressing and diagnosing the current issue of COVID 19 through lung auscultation.

Combining Social Media and Survey Data to Nowcast Migrant Stocks in the United States

Measuring and forecasting migration patterns has important implications for understanding broader population trends, for designing policy effectively and for allocating resources. However, data on migration and mobility are often lacking, and those that do exist are not available in a timely manner. Social media data offer new opportunities to provide more up-to-date demographic estimates and to complement more traditional data sources. Facebook’s Advertising Platform, for example, is a potentially rich data source of demographic information that is regularly updated. However, Facebook’s users are not representative of the underlying population. This paper proposes a statistical framework to combine social media data with traditional survey data to produce timely ‘nowcasts’ of migrant stocks by state in the United States. The model incorporates bias adjustment of Facebook data, and a pooled principal component time series approach, to account for correlations across age, time and space. We use the model to estimate and project migrants from Mexico, India and Germany, three migrant groups with varying levels and trends of migration in the US. By comparing short-term projections with data from the American Community Survey, we show that the model predictions outperform alternatives that rely solely on either social media or survey data.

Global autocorrelation test based on the Monte Carlo method and impacts of eliminating nonstationary components on the global autocorrelation test

Autocorrelation and non-stationarity are always concerned in analysis of meteorological and hydrological time series. Current commonly used methods, such as the Box-Pierce (BP) test and Ljung-Box (LB) test, always preset the maximum order for the autocorrelation significance test without considering the existence of high-order autocorrelation coefficient(s), and also neglect a fact that the sum of sample autocorrelation function is a constant value. Moreover, the impacts of autocorrelation on the significance test of nonstationary components of sample time series have drawn much attention, but less attention is paid to the impacts of eliminating nonstationary components on the global autocorrelation significance test. These issues are addressed in the paper. Based on the Monte Carlo method, a global autocorrelation test method, the quadratic sum (QS) test, is presented for judging the existence of high-order autocorrelation coefficient(s) of a sample time series. Besides, two nonparametric trend estimators (nonlinear and linear trend estimators) are employed to investigate the impacts of eliminating nonstationary components on the global autocorrelation test. The results show that (i) the QS test method is more robust than the BP test and LB test in verifying the existence of significant high-order autocorrelation coefficient(s); and (ii) eliminating a linear trend has less damage on the original global autocorrelation structure of sample time series by comparing with eliminating a nonlinear trend. Therefore, it is recommended to initially eliminate the linear trend from a sample time series, and then judge the existence of high-order autocorrelation coefficients of the time series based on the QS test.

A spatio-temporal attention-based spot-forecasting framework for urban traffic prediction

Spatio-temporal forecasting is an open research field whose interest is growing exponentially. In this work we focus on creating a complex deep neural framework for spatio-temporal traffic forecasting with comparatively very good performance and that shows to be adaptable over several spatio-temporal conditions while remaining easy to understand and interpret. Our proposal is based on an interpretable attention-based neural network in which several modules are combined in order to capture key spatio-temporal time series components. Through extensive experimentation, we show how the results of our approach are stable and better than those of other state-of-the-art alternatives.

Performance Analysis and Validation of Modified Singular Spectrum Analysis based on Simulation Torrential Rainfall Data

A popular method for time series analysis to extract the components of noise and trend from the time series data is called the singular spectrum analysis (SSA). However, the drawback of SSA is its problem in determining the appropriate window length, L for certain data set in confirming patent separation of the components of trend and noise. Another issue that crops up when using SSA is that, over time, the sum of day-to-day rainfall becomes nearly comparable. In this case, disjoints sets of singular values and distinctive series components could essentially be intermixed, resulting in poor separability between trend and noise components. The introduction of modified SSA is to mitigate the problems efficiently. The performance of modified SSA is measured by using w-correlation and RMSE based on simulated data. These results show that the parameter L = T/5 was suitable to use in short time series rainfall data. It can be proved by the plot of the extracted trend for modified SSA that appears to conform to the original data configuration for time series rainfall however there is the omission of components of noise predominantly for L = T/5 in detecting the uncharacteristically heavy downpour which could potentially initiate the occurrence of torrential rainfall. In addition, the result shows that average RMSE for reconstructed time series components of modified SSA is much smaller than SSA for each L

Weighted MLP-ARIMA series hybrid model for time series forecasting

With the increasing importance of forecasting with the utmost degree of accuracy, utilizing hybrid frameworks become a must for obtaining more accurate and more reliable forecasting results. Series hybrid methodology is one of the most widely-used hybrid approaches that has encountered a great amount of popularity in the literature of time series forecasting and has been applied successfully in a wide variety of domains. In such hybrid methods is assumed that there is an additive relationship among different components of time series. Thus, based on this assumption, various individual models can apply separately on decomposed components, and the final forecast can be obtained. However, developed series hybrid models in the literature are constructed based on the decomposing time series into linear and nonlinear parts and generating linear-nonlinear modeling order for decomposed parts. Another assumption considered in the traditional series model is assigning equal weights to each model used for modeling linear and nonlinear components. Thus, contrary to traditional series hybrid models, to improve the performance of series hybrid models, these two basic assumptions have been violated in this paper. This study aims to propose a novel weighted MLP-ARIMA model filling the gap of series hybrid models by changing the order of sequence modeling and assigning weight for each component. Firstly, the modeling order is changed to nonlinear-linear, and then Multi-Layer Perceptron Neural Network (MLPNN) -Auto-Regressive Integrated Moving Average(ARIMA) models are employed to model and process nonlinear and linear components respectively. Secondly, each model's weights are computed by the Ordinary Least Square (OLS) weighting algorithm. Thus, in this paper, a novel improved weighted MLP-ARIMA series hybrid model is proposed for time series forecasting. The real-world benchmark data sets, including Wolf's sunspot data, the Canadian lynx data, and the British pound/US dollar exchange rate data, are elected to verify the effectiveness of the proposed weighted MLP-ARIMA series hybrid model. The simulation results revealed that the weighted MLP-ARIMA model could obtain superior performance compared to ARIMA-MLP, MLP-ARIMA, as well as the ARIMA and MLPNN individual models. The proposed hybrid model can be an effective alternative to improve forecasting accuracy obtained by traditional series hybrid methods.