Expectation-Based Probabilistic Naive Approach for Forecasting Involving Optimized Parameter Estimation

Ahuja, Sahil; Kumar, Abhimanyu

doi:10.1007/s13369-022-06819-0

Cited by 2 publications

(3 citation statements)

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“…The supposition that its interest lies later in the period will be equivalent to the interest in the last period. The expression of the naïve method [ 37 , 40 , 41 ] is

The seasonal naïve model for the period t + k is of the form

where n is the period of seasonality and g is the integer part of k − 1/ n , which is the number of years prior to the forecast year t + k .…”

Section: Methodsmentioning

confidence: 99%

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Pakistan CO2 Emission Modelling and Forecasting: A Linear and Nonlinear Time Series Approach

Tawiah

Daniyal

Qureshi

2023

Journal of Environmental and Public Health

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Pakistan is considered among the top five countries with the highest CO2 emissions globally. This calls for pragmatic policy implementation by all stakeholders to bring finality to this alarming situation since it contributes greatly to global warming, thereby leading to climate change. This study is an attempt to make a comparative analysis of the linear time series models with nonlinear time series models to study CO2 emission data in Pakistan. These linear and nonlinear time series models were used to model and forecast future values of CO2 emissions for a short period. To assess and select the best model among these linear and nonlinear time series models, we used the root mean square error (RMSE) and the mean absolute error (MAE) as performance indicators. The outputs showed that the nonlinear machine learning models are the best among all other models, having the lowest RMSE and MAE values. Based on the forecasted value of the nonlinear machine learning neural network autoregressive model, Pakistan’s CO2 emissions will be 1.048 metric tons per capita by 2028. The increasing trend in emissions is a frightening and clear warning, suggesting that innovative policies must be initiated to reduce the trend. We encourage the Pakistan government to price CO2 emissions by companies and entities per ton, adapt electricity production from hydro, wind, and different sources with no emissions of CO2, initiate rigorous planting of more trees in the populated areas of Pakistan as forest covers, provide incentives to companies, organisations, institutions, and households to come out with clean technologies or use technologies with no CO2 emissions or those with lower ones, and fund more studies to develop clean and innovative technologies with less or no CO2 emissions.

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“…The supposition that its interest lies later in the period will be equivalent to the interest in the last period. The expression of the naïve method [ 37 , 40 , 41 ] is

The seasonal naïve model for the period t + k is of the form

where n is the period of seasonality and g is the integer part of k − 1/ n , which is the number of years prior to the forecast year t + k .…”

Section: Methodsmentioning

confidence: 99%

“…Te supposition that its interest lies later in the period will be equivalent to the interest in the last period. Te expression of the naïve method [37,40,41] is…”

Section: ) Trigonometric Seasonality Box-cox Transformation Arma Erro...mentioning

confidence: 99%

Pakistan CO2 Emission Modelling and Forecasting: A Linear and Nonlinear Time Series Approach

Tawiah

Daniyal

Qureshi

2023

Journal of Environmental and Public Health

View full text Add to dashboard Cite

show abstract

“…Metode NA memberikan hasil prediksi yang sama dengan nilai observasi masa lalu, secara matematis, didefiniskan sebagai berikut (Ahuja & Kumar, 2022) (Hoshmand, 2010):…”

Section: Naïve Approachunclassified

Prediksi Deret Waktu Manajemen Lalu Lintas

Albab¹,

Anjarsari²,

Febriyanti³

et al. 2023

AXIOMA

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Optimizing customer service for the number of drivers in an area through real-time transportation service industry online to scale up. In this paper, the dataset used is traffic management accompanied by attributes such as level 6 geohash, day, timestamp, and demand. The dataset used is a sample from geohash6 coded qp0991, containing online transportation demands from 01/04/2018 until 31/05/2018 (61 days). The training datasets are from the qp0991 code sample, starting from 01/04/2018 until 10/05/2018 and the remaining datasets are used as the testing datasets. The percentages for training and testing are respectively 67% and 33%. The methods applied to construct the objective function are three different forecasting methods, namely the Naïve approach, auto-regressive integrated moving average (ARIMA), and simple exponential smoothing. The results of this study indicate that the simple exponential smoothing method is better than the naïve approach and auto-regressive integrated moving average based on the root mean square error (RMSE), mean absolute error (MAE), and mean absolute percentage error (MAPE). The simple exponential smoothing has an accuracy rate of 98.7% for the RMSE value, 98.9% for the MAE value, and 88.81% for the MAPE value. Keywords: time series, traffic management

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Expectation-Based Probabilistic Naive Approach for Forecasting Involving Optimized Parameter Estimation

Cited by 2 publications

References 35 publications

Pakistan CO2 Emission Modelling and Forecasting: A Linear and Nonlinear Time Series Approach

Pakistan CO2 Emission Modelling and Forecasting: A Linear and Nonlinear Time Series Approach

Prediksi Deret Waktu Manajemen Lalu Lintas

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