Use of multivariate time series techniques to estimate the impact of particulate matter on the perceived annoyance

The UK has emerged as one of the largest producers of petroleum in the world. A significant amount of petroleum is used for fulfilling the energy demand within the country. However, the country witnessed a different trend from 2015. This is mainly due to the increase in imports of petroleum in order to meet domestic needs. To this, there is a need to identify the impact of changes exist in petrol and crude oil prices in the UK. In this context, the researcher has undertaken primary research to derive conclusions which are case specific and can comply with the research aim. The study used secondary data for the year 2015-2018 and conducted multivariate time series analysis. A series of tests including unit root, ARIMA, and co-integration tests were used to derive the results. The study found that there was an asymmetric relationship between the movements of prices of crude oil with respect to retail fuel prices in the long run. However, the study is not without limitations which are represented at the end of the study following with its future scope

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A Preliminary Assessment of the Impacts of Multiple Temporal-scale Variations in Particulate Matter on its Source Apportionment

10.5194/acp-2017-997 ◽

2018 ◽

Author(s):

Xing Peng ◽

Jian Gao ◽

Guoliang Shi ◽

Xurong Shi ◽

Yanqi Huangfu ◽

...

Keyword(s):

Time Series ◽

Particulate Matter ◽

Source Apportionment ◽

Chemical Species ◽

Principal Component ◽

Temporal Scale ◽

Mitigation Strategies ◽

Total Variance ◽

Secondary Source ◽

The Impact

Abstract. Time series of pollutant concentrations consist of variations at different time scales that are attributable to many processes/sources (data noise, source intensities, meteorological conditions, climate, etc.). Improving the knowledge of the impact of multiple temporal-scale components on pollutant variations and pollution levels can provide useful information for suitable mitigation strategies for pollutant control during a high pollution episode. To investigate the source factors driving these variations, the Kolmogorov-Zurbenko (KZ) filter was used to decompose the time series of PM2.5 (particulate matter with an aerodynamic diameter less than 2.5 μm) and chemical species into intra-day, diurnal, synoptic, and baseline temporal-scale components (TS components). The synoptic TS component has the largest amplitude and relative contributions (about 50 %) to the total variance of SO42−, NH4+, and OC concentrations. The diurnal TS component has the largest relative contributions to the total variance of PM2.5, NO3−, EC, Ca, and Fe concentrations, ranging from 32 % to 47 %. To investigate the source impacts on PM2.5 from different TS components, four datasets RI (intra-day removed), RD (diurnal removed), RS (synoptic removed), and RBL (baseline removed) were created by respectively removing the intra-day, diurnal, synoptic, and baseline TS component from the original datasets. Multilinear Engine 2 (ME-2) and/or principal component analysis was applied to these four datasets as well as the original datasets for source apportionment. ME-2 solutions using the original and RI dataset identify crustal dust contributions. For the solutions from original, RI, RD, and RS datasets, the total primary source impacts are close, ranging from 35.1 to 40.4 μg m−3 during the entire sampling period. For the secondary source impacts, solutions from the original, RI and RD dataset give similar source impacts (about 30 μg m−3), which were higher than the impacts derived from the RS datasets (21.2 μg m−3).

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Interactions of viral pathogens on hospital admissions for pneumonia, croup and chronic obstructive pulmonary diseases: results of a multivariate time-series analysis

Epidemiology and Infection ◽

10.1017/s0950268806006236 ◽

2006 ◽

Vol 134 (6) ◽

pp. 1174-1178 ◽

Cited By ~ 17

Author(s):

R. E. G. UPSHUR ◽

R. MOINEDDIN ◽

E. J. CRIGHTON ◽

M. MAMDANI

Keyword(s):

Time Series ◽

Granger Causality ◽

Hospital Admissions ◽

Multivariate Time Series ◽

Chronic Obstructive Lung Disease ◽

Epidemiological Data ◽

Chronic Obstructive ◽

Causality Test ◽

Syncytial Virus ◽

The Impact

Co-circulation of respiratory syncytial virus (RSV) and influenza has made the partitioning of morbidity and mortality from each virus difficult. Given the interaction between chronic obstructive lung disease (COPD) and pneumonia, often one can be mistaken for the other. Multivariate time-series methodology was applied to examine the impact of RSV and influenza on hospital admissions for bronchiolitis, pneumonia, and COPD. The Granger Causality Test, used to determine the causal relationship among series, showed that COPD and pneumonia are not influenced by RSV (P=0·2999 and 0·7725), but RSV does influence bronchiolitis (P=0·0001). Influenza was found to influence COPD, pneumonia, and bronchiolitis (P<0·0001). The use of multivariate time series and Granger causality applied to epidemiological data clearly illustrates the significant contribution of influenza and RSV to morbidity in the population.

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Data Augmentation with Suboptimal Warping for Time-Series Classification

Sensors ◽

10.3390/s20010098 ◽

2019 ◽

Vol 20 (1) ◽

pp. 98 ◽

Cited By ~ 3

Author(s):

Krzysztof Kamycki ◽

Tomasz Kapuscinski ◽

Mariusz Oszust

Keyword(s):

Time Series ◽

Data Augmentation ◽

Nearest Neighbor ◽

Multivariate Time Series ◽

Metric Learning ◽

Classification Performance ◽

Training Dataset ◽

Time Series Classification ◽

Extensive Evaluation ◽

The Impact

In this paper, a novel data augmentation method for time-series classification is proposed. In the introduced method, a new time-series is obtained in warped space between suboptimally aligned input examples of different lengths. Specifically, the alignment is carried out constraining the warping path and reducing its flexibility. It is shown that the resultant synthetic time-series can form new class boundaries and enrich the training dataset. In this work, the comparative evaluation of the proposed augmentation method against related techniques on representative multivariate time-series datasets is presented. The performance of methods is examined using the nearest neighbor classifier with the dynamic time warping (NN-DTW), LogDet divergence-based metric learning with triplet constraints (LDMLT), and the recently introduced time-series cluster kernel (NN-TCK). The impact of the augmentation on the classification performance is investigated, taking into account entire datasets and cases with a small number of training examples. The extensive evaluation reveals that the introduced method outperforms related augmentation algorithms in terms of the obtained classification accuracy.

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An Investigation Towards The Suitability Of Vector Autoregressive Approach On Modeling Meteorological Data

Modern Applied Science ◽

10.5539/mas.v9n11p89 ◽

2015 ◽

Vol 9 (11) ◽

pp. 89 ◽

Cited By ~ 1

Author(s):

Siti Mariam Norrulashikin

Keyword(s):

Time Series ◽

Meteorological Data ◽

Multivariate Time Series ◽

Model Identification ◽

Suitable Model ◽

Multiple Time ◽

Vector Autoregressive ◽

Modeling Strategy ◽

Vector Time Series ◽

The Impact

In most meteorological problems, two or more variables evolve over time. These variables not only haverelationships with each other, but also depend on each other. Although in many situations the interest was onmodelling single variable as a vector time series without considering the impact other variables have on it. Thevector autoregression (VAR) approach to multiple time series analysis are potentially useful in many types ofsituations which involve the building of models for discrete multivariate time series. This approach has 4important stages of the process that are data pre-processing, model identification, parameter estimation, andmodel adequacy checking. In this research, VAR modeling strategy was applied in modeling three variables ofmeteorological variables, which include temperature, wind speed and rainfall data. All data are monthly data,taken from the Kuala Krai station from January 1985 to December 2009. Two models were suggested byinformation criterion procedures, however VAR (3) model is the most suitable model for the data sets based onthe model adequacy checking and accuracy testing.

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Effect of socioeconomic status on the association between air pollution and mortality in Bogota, Colombia

Salud Pública de México ◽

10.21149/spm.v56i4.7357 ◽

2014 ◽

Vol 56 (4) ◽

pp. 371 ◽

Cited By ~ 3

Author(s):

Luis Camilo Blanco-Becerra ◽

Víctor Miranda-Soberanis ◽

Albino Barraza-Villarreal ◽

Washington Junger ◽

Magali Hurtado-Díaz ◽

...

Keyword(s):

Air Pollution ◽

Socioeconomic Status ◽

Time Series ◽

Particulate Matter ◽

Ecological Study ◽

Aerodynamic Diameter ◽

Components Analysis ◽

The Impact ◽

The Relationship ◽

Respiratory Causes

Objective. To evaluate the modification effect of socioeconomic status (SES) on the association between acute exposure to particulate matter less than 10 microns in aerodynamic diameter (PM10) and mortality in Bogota, Colombia. Materials and methods. A time-series ecological study was conducted (1998-2006). The localities of the cities were stratified using principal components analysis, creating three levels of aggregation that allowed for the evaluation of the impact of SES on the relationship between mortality and air pollution. Results. For all ages, the change in the mortality risk for all causes was 0.76% (95%CI 0.27-1.26) for SES I (low), 0.58% (95%CI 0.16-1.00) for SES II (mid) and -0.29% (95%CI -1.16-0.57) for SES III (high) per 10µg/m3 increment in the daily average of PM10 on day of death. Conclusions. The results suggest that SES significantly modifies the effect of environmental exposure to PM10 on mortality from all causes and respiratory causes.

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COVID-19 Sentiments and Impact on Stock Market Prices

International Journal of Data Analytics ◽

10.4018/ijda.2021070103 ◽

2021 ◽

Vol 2 (2) ◽

pp. 40-58

Author(s):

Chandra Prayaga ◽

Krishna Devulapalli ◽

Lakshmi Prayaga ◽

Aaron Wade

Keyword(s):

Time Series ◽

Stock Market ◽

Stock Prices ◽

Multivariate Time Series ◽

Market Prices ◽

Vector Error Correction ◽

Jones Index ◽

Vector Error ◽

The Impact ◽

Stock Values

This paper studies the impact of sentiments expressed by tweets from Twitter on the stock market associated with COVID-19 during the critical period from December 1, 2019 to May 31, 2020. The stock prices of 30 companies on the Dow Jones Index were collected for this period. Twitter tweets were also collected, using the search phrases “COVID-19” and “Corona Virus” for the same period, and their sentiment scores were calculated. The three time series, open and close stock values, and the corresponding sentiment scores from tweets were sorted by date and combined. Multivariate time series models based on vector error correction (VEC) models were applied to this data. Forecasts for these 30 companies were made for the time series open, for the 30 days of June 2020, following the data collection period. Stock market data for the month of June was for all the companies was compared with the forecast from the model. These were found to be in excellent agreement, implying that sentiment had a significant impact or was significantly impacted by the stock market prices.

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Forecasting time series with multivariate copulas

Dependence Modeling ◽

10.1515/demo-2015-0005 ◽

2015 ◽

Vol 3 (1) ◽

Cited By ~ 3

Author(s):

Clarence Simard ◽

Bruno Rémillard

Keyword(s):

Time Series ◽

Multivariate Time Series ◽

Financial Data ◽

Practical Implementation ◽

Estimation Errors ◽

Marginal Distributions ◽

Test Of Independence ◽

Multivariate Method ◽

Forecasting Method ◽

The Impact

AbstractIn this paper we present a forecasting method for time series using copula-based models for multivariate time series. We study how the performance of the predictions evolves when changing the strength of the different possible dependencies, as well as the structure of the dependence. We also look at the impact of the marginal distributions. The impact of estimation errors on the performance of the predictions is also considered. In all the experiments, we compare predictions from our multivariate method with predictions from the univariate version which has been introduced in the literature recently. To simplify implementation, a test of independence between univariate Markovian time series is proposed. Finally, we illustrate the methodology by a practical implementation with financial data.

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Sensor Classification Using Convolutional Neural Network by Encoding Multivariate Time Series as Two-Dimensional Colored Images

Sensors ◽

10.3390/s20010168 ◽

2019 ◽

Vol 20 (1) ◽

pp. 168 ◽

Cited By ~ 4

Author(s):

Chao-Lung Yang ◽

Zhi-Xuan Chen ◽

Chen-Yi Yang

Keyword(s):

Neural Network ◽

Time Series ◽

Convolutional Neural Network ◽

Classification Accuracy ◽

Multivariate Time Series ◽

Complex Structure ◽

Series Data ◽

Two Dimensional ◽

Transformation Methods ◽

The Impact

This paper proposes a framework to perform the sensor classification by using multivariate time series sensors data as inputs. The framework encodes multivariate time series data into two-dimensional colored images, and concatenate the images into one bigger image for classification through a Convolutional Neural Network (ConvNet). This study applied three transformation methods to encode time series into images: Gramian Angular Summation Field (GASF), Gramian Angular Difference Field (GADF), and Markov Transition Field (MTF). Two open multivariate datasets were used to evaluate the impact of using different transformation methods, the sequences of concatenating images, and the complexity of ConvNet architectures on classification accuracy. The results show that the selection of transformation methods and the sequence of concatenation do not affect the prediction outcome significantly. Surprisingly, the simple structure of ConvNet is sufficient enough for classification as it performed equally well with the complex structure of VGGNet. The results were also compared with other classification methods and found that the proposed framework outperformed other methods in terms of classification accuracy.

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Two Time-Series Analyses of the Impact of Antibiotic Consumption and Alcohol-Based Hand Disinfection on the Incidences of Nosocomial Methicillin-ResistantStaphylococcus aureusInfection andClostridium difficileInfection

Infection Control and Hospital Epidemiology ◽

10.1086/596605 ◽

2009 ◽

Vol 30 (4) ◽

pp. 346-353 ◽

Cited By ~ 70

Author(s):

Klaus Kaier ◽

Christian Hagist ◽

Uwe Frank ◽

Andreas Conrad ◽

Elisabeth Meyer

Keyword(s):

Time Series ◽

Multivariate Analysis ◽

Multivariate Time Series ◽

Antibiotic Consumption ◽

Third Generation ◽

Hand Disinfection ◽

Mrsa Infection ◽

Time Series Analyses ◽

The Impact ◽

Third Generation Cephalosporins

Objective.To determine the impact of antibiotic consumption and alcohol-based hand disinfection on the incidences of nosocomial methicillin-resistantStaphylococcus aureus(MRSA) infection andClostridium difficileinfection (CDI).Methods.Two multivariate time-series analyses were performed that used as dependent variables the monthly incidences of nosocomial MRSA infection and CDI at the Freiburg University Medical Center during the period January 2003 through October 2007. The volume of alcohol-based hand rub solution used per month was quantified in liters per 1,000 patient-days. Antibiotic consumption was calculated in terms of the number of defined daily doses per 1,000 patient-days per month.Results.The use of alcohol-based hand rub was found to have a significant impact on the incidence of nosocomial MRSA infection (P<.001). The multivariate analysis (R2= 0.66) showed that a higher volume of use of alcohol-based hand rub was associated with a lower incidence of nosocomial MRSA infection. Conversely, a higher level of consumption of selected antimicrobial agents was associated with a higher incidence of nosocomial MRSA infection. This analysis showed this relationship was the same for the use of second-generation cephalosporins (P= .023), third-generation cephalosporins (P= .05), fluoroquinolones (P= .01), and lincosamides (P= .05). The multivariate analysis (R2= 0.55) showed that a higher level of consumption of third-generation cephalosporins (P= .008), fluoroquinolones (P= .084), and/or macrolides (P= .007) was associated with a higher incidence of CDI. A correlation with use of alcohol-based hand rub was not detected.Conclusion.In 2 multivariate time-series analyses, we were able to show the impact of hand hygiene and antibiotic use on the incidence of nosocomial MRSA infection, but we found no association between hand hygiene and incidence of CDI.

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