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Using the R-MAPE index as a resistant measure of forecast accuracy
7 references·4 citing·34 second hop
Using the R-MAPE index as a resistant measure of forecast accuracy (2013)Using the R-MAPE index as a resistant mea…Reliability and Validity Assessment (2011)Reliability and Validity Asse…Designing a neural network for forecasting financial and economic time series (1996)Designing a neural network fo…Forecasting methods and applications (1993)Forecasting methods and appli…Error measures for generalizing about forecasting methods: Empirical comparisons (1992)Error measures for generalizi…Time series forecasting using neural networks vs. Box- Jenkins methodology (1991)Time series forecasting using…Industrial and business forecasting methods (1982)Industrial and business forec…Time Series Analysis: Forecasting and Control (1977)Time Series Analysis: Forecas…Impact of the COVID-19 Vaccination on Bike Sharing Demand in NYC: ARIMAX and Bayesian Time Series Analyses (2023)Impact of the COVID-19 Vaccin…Exploring Patterns of Transportation-Related CO2 Emissions Using Machine Learning Methods (2022)Exploring Patterns of Transpo…Evaluation and analysis of the projected population of China (2022)Evaluation and analysis of th…Predicting LQ45 financial sector indices using RNN-LSTM (2021)Predicting LQ45 financial sec…A neural network model for bankruptcy prediction (1990)Support vector machine with adaptive parameters in financial time series forecasting (2003)Predicting the Price of Bitcoin Using Machine Learning (2018)Forecasting: theory and practice (2022)The accuracy of extrapolation (time series) methods: Results of a forecasting competition (1982)A state space framework for automatic forecasting using exponential smoothing methods (2002)Making and Evaluating Point Forecasts (2011)Predicting stock market index using fusion of machine learning techniques (2014)Stock market's price movement prediction with LSTM neural networks (2017)NSE Stock Market Prediction Using Deep-Learning Models (2018)Machine learning models predicting returns: why most popular performance metrics are misleading and proposal for an efficient metric (2021)Multivariate cryptocurrency prediction: comparative analysis of three recurrent neural networks approaches (2022)Stock price forecasting in Indonesia stock exchange using deep learning: a comparative study (2023)The human core of the shared socioeconomic pathways: Population scenarios by age, sex and level of education for all countries to 2100 (2014)Spatially explicit global population scenarios consistent with the Shared Socioeconomic Pathways (2016)Fertility, mortality, migration, and population scenarios for 195 countries and territories from 2017 to 2100: a forecasting analysis for the Global Burden of Disease Study (2020)The regeneration path not taken further: An experiment in urban densification and state entrepreneurialism in a resettlement neighborhood in Suzhou, China (2025)Transport sector CO2 emissions growth in Asia: Underlying factors and policy options (2009)Effect of transportation infrastructure on economic growth in India: The VECM approach (2012)A new metric of absolute percentage error for intermittent demand forecasts (2016)A Review of Scale Development Practices in the Study of Organizations (1995)A Brief Tutorial on the Development of Measures for Use in Survey Questionnaires (1998)A model of destination image formation (1999)Neural network techniques for financial performance prediction: integrating fundamental and technical analysis (2003)Designing an artificial neural network for forecasting tourism time series (2005)Improving artificial neural networks’ performance in seasonal time series forecasting (2008)25 years of time series forecasting (2006)Statistical and Machine Learning forecasting methods: Concerns and ways forward (2018)Distribution of the Estimators for Autoregressive Time Series with a Unit Root (1979)Likelihood Ratio Statistics for Autoregressive Time Series with a Unit Root (1981)Generalized autoregressive conditional heteroskedasticity (1986)Modeling and forecasting tourism demand for arrivals with stochastic nonstationary seasonality and intervention (2002)Modeling and forecasting the CO2 emissions, energy consumption, and economic growth in Brazil (2011)Forecasting of CO2 emissions, energy consumption and economic growth in China using an improved grey model (2012)
this work references cited by second hop

References

  • 1977Time Series Analysis: Forecasting and Control19,200 cited
  • 2011Reliability and Validity Assessment5,040 cited
  • 1993Forecasting methods and applications2,393 cited
  • 1992Error measures for generalizing about forecasting methods: Empirical comparisons1,400 cited
  • 1996Designing a neural network for forecasting financial and economic time series1,092 cited
  • 1982Industrial and business forecasting methods515 cited
  • 1991Time series forecasting using neural networks vs. Box- Jenkins methodology407 cited

Cited by

  • 2021Predicting LQ45 financial sector indices using RNN-LSTM72 cited
  • 2022Exploring Patterns of Transportation-Related CO2 Emissions Using Machine Learning Methods64 cited
  • 2022Evaluation and analysis of the projected population of China47 cited
  • 2023Impact of the COVID-19 Vaccination on Bike Sharing Demand in NYC: ARIMAX and Bayesian Time Series Analyses5 cited

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