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Forecasting municipal waste accumulation rate and personal consumption expenditures using vector autoregressive (VAR) model

Bień Jurand

Production Engineering Archives

2022

Vol. 28, Iss. 2

150--156

Accurate forecasting of municipal solid waste (MSW) generation is important for the planning, operation and optimization of municipal waste management system. However, it’s not easy task due to dynamic changes in waste volume, its composition or unpredictable factors. Initially, mainly conventional and descriptive statistical models of waste generation forecasting with demographic and socioeconomic factors were used. Methods based on machine learning or artificial intelligence have been widely used in municipal waste projection for several years. This study investigates the trend of municipal waste accumulation rate and its relation to personal consumption expenditures based on the yearly data achieved from Local Data Bank (LDB) driven by Polish Statistical Office. The effect of personal consumption expenditures on the municipal waste accumulation rate was analysed by using the vector autoregressive model (VAR). The results showed that such method can be successfully used for this purpose with an approximate level of 2.3% Root Mean Square Error (RMSE).

Prediction intervals and regions for multivariate time series models with sieve bootstrap

Różański R., Chłapiński G., Hławka M., Jamróz K., Kawecki M., Zagdański A.

Probability and Mathematical Statistics

2018

Vol. 38, Fasc. 2

317--357

In the paper, the construction of unconditional bootstrap prediction intervals and regions for some class of second order stationary multivariate linear time series models is considered. Our approach uses the sieje bootstrap procedure introduced by Kreiss (1992) and Bühlmann (1997). Basic theoretical results concerning consistency of the bootstrap replications and the bootstrap prediction regions are proved. We present a simulation study comparing the proposed bootstrap methods with the Box-Jenkins approach.