Multivariate Time Series Modelling Approach for Production Forecasting in Unconventional Resources

Temporal variation in species abundances occurs in all ecological communities. Here, we explore the role that this temporal turnover plays in maintaining assemblage diversity. We investigate a three-decade time series of estuarine fishes and show that the abundances of the individual species fluctuate asynchronously around their mean levels. We then use a time-series modelling approach to examine the consequences of different patterns of turnover, by asking how the correlation between the abundance of a species in a given year and its abundance in the previous year influences the structure of the overall assemblage. Classical diversity measures that ignore species identities reveal that the observed assemblage structure will persist under all but the most extreme conditions. However, metrics that track species identities indicate a narrower set of turnover scenarios under which the predicted assemblage resembles the natural one. Our study suggests that species diversity metrics are insensitive to change and that measures that track species ranks may provide better early warning that an assemblage is being perturbed. It also highlights the need to incorporate temporal turnover in investigations of assemblage structure and function.

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Multivariate time series modelling for urban air quality

Urban Climate ◽

10.1016/j.uclim.2021.100834 ◽

2021 ◽

Vol 37 ◽

pp. 100834

Author(s):

Hajar Hajmohammadi ◽

Benjamin Heydecker

Keyword(s):

Time Series ◽

Air Quality ◽

Multivariate Time Series ◽

Urban Air Quality ◽

Urban Air ◽

Time Series Modelling

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A Review on Short-Term Prediction of Air Pollutant Concentrations

International Journal of Engineering & Technology ◽

10.14419/ijet.v7i3.23.17254 ◽

2018 ◽

Vol 7 (3.23) ◽

pp. 32

Author(s):

Ahmad Fauzi Raffee ◽

Siti Nazahiyah Rahmat ◽

Hazrul Abdul Hamid ◽

Muhammad Ismail Jaffar

Keyword(s):

Air Pollution ◽

Time Series ◽

Multivariate Time Series ◽

Air Pollutant ◽

Short Term ◽

Pollutant Concentrations ◽

Univariate Time Series ◽

Time Series Modelling ◽

Air Pollutant Concentrations ◽

Short Term Prediction

In the attempt to increase the production of the industrial sector to accommodate human needs; motor vehicles and power plants have led to the decline of air quality. The tremendous decline of air pollution levels can adversely affect human health, especially children, those elderly, as well as patients suffering from asthma and respiratory problems. As such, the air pollution modelling appears to be an important tool to help the local authorities in giving early warning, apart from functioning as a guide to develop policies in near future. Hence, in order to predict the concentration of air pollutants that involves multiple parameters, both artificial neural network (ANN) and principal component regression (PCR) have been widely used, in comparison to classical multivariate time series. Besides, this paper also presents comprehensive literature on univariate time series modelling. Overall, the classical multivariate time series modelling has to be further investigated so as to overcome the limitations of ANN and PCR, including univariate time series methods in short-term prediction of air pollutant concentrations.

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Multivariate Time Series Modelling with Seasonal Univariate Components; Evidence from Nigeria GDP

Asian Journal of Probability and Statistics ◽

10.9734/ajpas/2019/v5i430140 ◽

2019 ◽

pp. 1-20

Author(s):

Taofikat Abidemi Azeez ◽

Yusuf Olufemi Olusola ◽

Hamzat Kayode Idris ◽

Salawu Monsuru Micheal

Keyword(s):

Time Series ◽

Multivariate Time Series ◽

Positive Association ◽

Information Criteria ◽

Strong Positive Correlation ◽

Positive Correlation ◽

Pairwise Correlation ◽

Time Series Modelling ◽

Adf Test ◽

Time Plot

The patterns of GDP variables are graphically examined using time plot presented the time plot for the GDP variables concerning time presented a combined single time plot for all the considered GDP variables. The relationship, as well as the degree of relationship between/among the GDP variables, was further revealed by computing the pairwise correlation. Based on the output, each variable when crossed classified with itself have a strong positive correlation with an output of (1), while pairwise correlation reveals a positive figure with the least estimate being (0.3149), this implies that for all the variables there exist a positive correlation. All the pairwise relationship reveals a strong positive association with all the estimates revealing a value between (0.8-0.9) except ‘trade and industry' that shows a positive relationship but not strong with an estimate of (0.3149). The initial test in fitting a time series model is to examine the series for stationarity. The Augmented Dickey-Fuller test revealed that ‘Agriculture’, ‘Construction’, and Services’” satisfies the requirement of stationarity while the series ‘industry and “Trade” are non-stationary initially but later became stationary after the application of the first difference transformation which was confirmed after the application of the ADF test to the first differenced series. The Johansen co-integration's Trace test was employed to determine the order of co-integration and it was revealed that the series are cointegrated hence presentation of the equation of integration. We presented the lag length estimation criteria which revealed that the lag length of order 5 is appropriate for the VAR model as suggested by Akaike Information Criteria (AIC), Hannan-Quinn (HQ) Information Criteria, Schwarz Information Criteria (SC). The VAR(5) model was fitted for all the considered GDP variables.

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