Time Series Analysis - 931 Words

The existing literature proposes various methodologies and procedures to predict GHG emissions in the transport sector. In general, these studies utilize time series analysis, regression analysis, decomposition, and optimization models, as explained below: †¢ Time series analysis (Sultan 2010) introduces the use of co-combination of pay per capita and fuel price (FP) to measure transport fuel consumption (FC), while (Bekhet, H Yasmin 2013), (Bekhet, HA Yusop 2009), (Ang 2008), (Ediger Akar 2007), and (Wang, SS et al. 2011) discover a relationship between vitality utilization and CO2 discharges. (Begum et al. 2015) consider the impact of GDP, FC, and concentration of population on the CO2 emissions. (Ivy-Yap, LL Bekhet 2015)†¦show more content†¦Ã¢â‚¬ ¢ Strategic planning tools Greaves (2009) evaluates the impacts of air quality and GHG reduction using a strategic-level modeling tool that considers freight travel, the characteristics of the fleet, and the factors related to GHG and non-GHG emissions. †¢ Linear programming models Researchers have also proposed a number of optimisation models to predict GHG emissions, especially from the energy sectors. (Bà ¶rjesson Ahlgren 2012), (Bai Wei 1996), and (Wang, C et al. 2008) explore the cost-effectiveness of conceivable CO2 reduction choices for the energy industries. Furthermore, utilizing a mixed integer linear programming model, (Hashim et al. 2005) contemplate the impacts of fuel balancing and fuel switching choices on power generation. Their studies reveal that FE and fuel switching are the best choices to decrease CO2 discharges. (Tan et al. 2013) utilize mixed integer linear programming analysis to perfectly arrange waste to the level of vitality that best minimizes electricity generation costs and CO2 emissions. Generally, after a period of time, the road transport sector’s GHG outflows demonstrate a pattern. Therefore, through the use of statistical forecasting techniques, researchers and planners can anticipate future outflows. After (Brown 1957) andShow MoreRelatedTime Series Analysis: the Multiplicative Decomposition Method1432 Words   |  6 PagesTime Series Analysis: The Multiplicative Decomposition Method Table of Contents Page Abstract†¦Ã¢â‚¬ ¦Ã¢â‚¬ ¦Ã¢â‚¬ ¦Ã¢â‚¬ ¦Ã¢â‚¬ ¦Ã¢â‚¬ ¦Ã¢â‚¬ ¦Ã¢â‚¬ ¦Ã¢â‚¬ ¦Ã¢â‚¬ ¦Ã¢â‚¬ ¦Ã¢â‚¬ ¦Ã¢â‚¬ ¦Ã¢â‚¬ ¦Ã¢â‚¬ ¦Ã¢â‚¬ ¦Ã¢â‚¬ ¦Ã¢â‚¬ ¦Ã¢â‚¬ ¦Ã¢â‚¬ ¦Ã¢â‚¬ ¦Ã¢â‚¬ ¦Ã¢â‚¬ ¦Ã¢â‚¬ ¦Ã¢â‚¬ ¦Ã¢â‚¬ ¦Ã¢â‚¬ ¦Ã¢â‚¬ ¦Ã¢â‚¬ ¦Ã¢â‚¬ ¦Ã¢â‚¬ ¦Ã¢â‚¬ ¦Ã¢â‚¬ ¦Ã¢â‚¬ ¦Ã¢â‚¬ ¦Ã¢â‚¬ ¦Ã¢â‚¬ ¦Ã¢â‚¬ ¦Ã¢â‚¬ ¦Ã¢â‚¬ ¦Ã¢â‚¬ ¦Ã¢â‚¬ ¦Ã¢â‚¬ ¦Ã¢â‚¬ ¦Ã¢â‚¬ ¦Ã¢â‚¬ ¦Ã¢â‚¬ ¦Ã¢â‚¬ ¦Ã¢â‚¬ ¦Ã¢â‚¬ ¦Ã¢â‚¬ ¦.3 Introduction†¦Ã¢â‚¬ ¦Ã¢â‚¬ ¦Ã¢â‚¬ ¦Ã¢â‚¬ ¦Ã¢â‚¬ ¦Ã¢â‚¬ ¦Ã¢â‚¬ ¦Ã¢â‚¬ ¦Ã¢â‚¬ ¦Ã¢â‚¬ ¦Ã¢â‚¬ ¦Ã¢â‚¬ ¦Ã¢â‚¬ ¦Ã¢â‚¬ ¦Ã¢â‚¬ ¦Ã¢â‚¬ ¦Ã¢â‚¬ ¦Ã¢â‚¬ ¦Ã¢â‚¬ ¦Ã¢â‚¬ ¦Ã¢â‚¬ ¦Ã¢â‚¬ ¦Ã¢â‚¬ ¦Ã¢â‚¬ ¦Ã¢â‚¬ ¦Ã¢â‚¬ ¦Ã¢â‚¬ ¦Ã¢â‚¬ ¦Ã¢â‚¬ ¦Ã¢â‚¬ ¦Ã¢â‚¬ ¦Ã¢â‚¬ ¦Ã¢â‚¬ ¦Ã¢â‚¬ ¦Ã¢â‚¬ ¦Ã¢â‚¬ ¦Ã¢â‚¬ ¦Ã¢â‚¬ ¦Ã¢â‚¬ ¦Ã¢â‚¬ ¦Ã¢â‚¬ ¦Ã¢â‚¬ ¦Ã¢â‚¬ ¦Ã¢â‚¬ ¦...†¦4-5 Methodology: Multiplicative Decomposition†¦Ã¢â‚¬ ¦Ã¢â‚¬ ¦Ã¢â‚¬ ¦Ã¢â‚¬ ¦Ã¢â‚¬ ¦Ã¢â‚¬ ¦Ã¢â‚¬ ¦Ã¢â‚¬ ¦Ã¢â‚¬ ¦Ã¢â‚¬ ¦Ã¢â‚¬ ¦Ã¢â‚¬ ¦Ã¢â‚¬ ¦Ã¢â‚¬ ¦Ã¢â‚¬ ¦Ã¢â‚¬ ¦Ã¢â‚¬ ¦.†¦5-7 Advantages/Disadvantages of Multiplicative Method†¦Ã¢â‚¬ ¦Ã¢â‚¬ ¦Ã¢â‚¬ ¦Ã¢â‚¬ ¦Ã¢â‚¬ ¦Ã¢â‚¬ ¦Ã¢â‚¬ ¦Ã¢â‚¬ ¦Ã¢â‚¬ ¦Ã¢â‚¬ ¦Ã¢â‚¬ ¦7-8 Conclusion†¦Ã¢â‚¬ ¦Ã¢â‚¬ ¦Ã¢â‚¬ ¦Ã¢â‚¬ ¦Ã¢â‚¬ ¦Ã¢â‚¬ ¦Ã¢â‚¬ ¦Ã¢â‚¬ ¦Ã¢â‚¬ ¦Ã¢â‚¬ ¦Ã¢â‚¬ ¦Ã¢â‚¬ ¦Ã¢â‚¬ ¦Ã¢â‚¬ ¦Ã¢â‚¬ ¦Ã¢â‚¬ ¦Ã¢â‚¬ ¦Ã¢â‚¬ ¦Ã¢â‚¬ ¦Ã¢â‚¬ ¦Ã¢â‚¬ ¦Ã¢â‚¬ ¦Ã¢â‚¬ ¦Ã¢â‚¬ ¦Ã¢â‚¬ ¦Ã¢â‚¬ ¦Ã¢â‚¬ ¦Ã¢â‚¬ ¦Ã¢â‚¬ ¦Ã¢â‚¬ ¦Ã¢â‚¬ ¦Ã¢â‚¬ ¦Ã¢â‚¬ ¦Ã¢â‚¬ ¦Ã¢â‚¬ ¦Ã¢â‚¬ ¦Ã¢â‚¬ ¦Ã¢â‚¬ ¦Ã¢â‚¬ ¦Ã¢â‚¬ ¦Ã¢â‚¬ ¦Ã¢â‚¬ ¦Ã¢â‚¬ ¦Ã¢â‚¬ ¦Ã¢â‚¬ ¦Ã¢â‚¬ ¦Ã¢â‚¬ ¦Ã¢â‚¬ ¦..8 Abstract One of the most essential piecesRead MoreEvaluation Of The Testing Techniques1550 Words   |  7 Pagesfinancial time series is stationary. 4.3.2 Unit Root Test The Unit Root Test, also named Augmented Dickey-Fuller Test (ADF Test), is put forward according to whether the macroeconomic datum or financial datum has some special characteristics, which is a particular method to test stationarity of the financial time series (Choi, 2015). To put it simply, testing the unit root is to examine whether there will be a unit root in the analysis of time series or not. The financial time series would be consideredRead MoreApplication Of A Consultant For Excellent Consulting Group1335 Words   |  6 Pagesforecasting methods. In this case we have decided to look at the sales data for client’s lottery app as a single data set and use a time series analysis, namely SES, single exponential smoothing. The simple exponential smoothing (SES) model is usually based on the premise that the level of time series should ï ¬â€šuctuate about a constant level or change slowly over the time (Ostertagova Ostertag, 2012). An excel spreadsheet and simple exponential smoothing will be utilized to help analyze theRead MoreImpact Of Tourism On Sri Lanka1310 Words   |  6 PagesSource: Butler (1980) Model of Tourist Arrivals The analysis and forecasting of common time series can be done by the non-seasonal ARIMA(p,d,q) (Box et al., 1994) process is given by ã€â€"∅(B)(1-B)ã€â€"^d y_t=c+ÃŽ ¸(B) a_t, where {a_t} is a white noise process with mean 0 and variance ÏÆ'^2, B is the backshift operator, and ∅(z) and ÃŽ ¸(z) are polynomials of orders p and q with no common factors and d is the order of differencing. To deal with series containing seasonal fluctuations, Box-Jenkins recommendedRead MoreAssociative and Time Series Forecasting Models1514 Words   |  7 PagesForecasting Models: Associative and Time Series Forecasting involves using past data to generate a number, set of numbers, or scenario that corresponds to a future occurrence. It is absolutely essential to short-range and long-range planning. Time Series and Associative models are both quantitative forecast techniques are more objective than qualitative techniques such as the Delphi Technique and market research. Time Series Models Based on the assumption that history will repeat itself,Read MoreRegression Analysis of Dependent Variables1183 Words   |  5 Pagesresults of regression analysis carried out with the dependent variables of cnx_auto, cnx_auto, cnx_bank, cnx_energy, cnx_finance, cnx_fmcg, cnx_it, cnx_metal, cnx_midcap, cnx_nifty, cnx_psu_bank, cnx_smallcap and with the independent variables such as CPI, Forex_Rates_USD, GDP, Gold, Silver, WPI_inflation. The coefficient of determination, denoted R ² and pronounced as R squared, indicates how well data points fit a statistical model and the adjusted R ² values in the analysis are fairly good whichRead MoreRelationship between Inflation Rate and Unemployment in Malaysia1110 Words   |  4 Pagesresearch model, while the methodology includes research methodology such as unit root test, cointegration test, and Wald test. They are applied in the data analysis in order to examine and recognize the relationship between inflation and unemployment rate. In this research, the ADF unit root test is used to test the stationary properties of the time series. For the ADf unit root test, the result indicates that inflation and unemployment are non-stationary in the level but being stationary in the first differenceRead MoreThe Effect Of Monetary Policy On Determination Of Coal Prices1013 Words   |  5 Pagesthat the selected variables must be nonstationary (i.e., I(1) series). The presence of a unit root in the variables is thus tested using the Dickey Fuller generalized least squares (DF-GLS) test (Elliott et al., 1996). Panel A of Table 1 reports the results of the DF-GLS test. Since the null hypothesis of a unit root cannot (can) be rejected for any of the levels (first differences) of the three variables at the 5% level, all the series are found to be nonstationary I(1) processes. It should be emphasizedRead MoreThe Role Of Indian Fdi On Nepalese Economic Growth1252 Words   |  6 Pagesregressors innovations. This method is used to modify the least squares to account for serial correlation effects and for the endogeneity in the regressions that result from the existence of cointegrating relationship. Consider the n+1 dimensional time series vector process (y_t,X_t) with following cointegrating equation. y_t=ã€â€"X_tã€â€"^ ÃŽ ²+ã€â€"D_1tã€â€"^ ÃŽ ³_1+u_1t (1) Where, D_t=(ã€â€"D_1tã€â€"^ ,ã€â€"D_2tã€â€"^ ) areRead MoreHausman, Autocorrelation Test and Heteroscedasticity, Paragraphs542 Words   |  2 Pagesestimates and consistent results owing to the more efficient model. Autocorrelation test Another terms sometimes used for describe Autocorrelation these are â€Å"lagged correlation† or â€Å"serial correlation†, which denotes to the connection among members of a series of numbers arranged in period. Positive autocorrelation might be considered a specific form of â€Å"persistence†, a tendency for a system to remain in the same state from one observation to the next. For example, the likelihood of tomorrow being rainy