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Knowing what to expect, forecasting monthly emergency department visits: A time-series analysis
6 references·2 citing·27 second hop
Knowing what to expect, forecasting monthly emergency department visits: A time-series analysis (2013)Knowing what to expect, forecasting month…forecast: Forecasting Functions for Time Series and Linear Models (2009)forecast: Forecasting Functio…Forecasting Daily Patient Volumes in the Emergency Department (2008)Forecasting Daily Patient Vol…Automatic Time Series Forecasting: The forecast Package for R (2008)Automatic Time Series Forecas…Forecasting emergency department presentations (2007)Forecasting emergency departm…A state space framework for automatic forecasting using exponential smoothing methods (2002)A state space framework for a…The M3-Competition: results, conclusions and implications (2000)The M3-Competition: results, …Medical service demand forecasting using a hybrid model based on ARIMA and self-adaptive filtering method (2020)Medical service demand foreca…Forecasting of weekly patient visits to emergency department: real case study (2019)Forecasting of weekly patient…Time Series Analysis: Forecasting and Control (1977)25 years of time series forecasting (2006)Prediction of Daily Patient Numbers for a Regional Emergency Medical Center using Time Series Analysis (2010)Applications of artificial intelligence for disaster management (2020)Combining forecasts: A review and annotated bibliography (1989)Statistical and Machine Learning forecasting methods: Concerns and ways forward (2018)Recurrent Neural Networks for Time Series Forecasting: Current status and future directions (2020)Likelihood Ratio Statistics for Autoregressive Time Series with a Unit Root (1981)Testing the null hypothesis of stationarity against the alternative of a unit root (1992)Another look at measures of forecast accuracy (2006)DeepAR: Probabilistic forecasting with autoregressive recurrent networks (2019)Exponential smoothing: The state of the art (1985)Evolving forecasting classifications and applications in health forecasting (2012)A unified machine learning approach to time series forecasting applied to demand at emergency departments (2021)The accuracy of extrapolation (time series) methods: Results of a forecasting competition (1982)Forecasting methods and applications (1993)Forecasting Time Series With Complex Seasonal Patterns Using Exponential Smoothing (2011)A new metric of absolute percentage error for intermittent demand forecasts (2016)The M4 Competition: 100,000 time series and 61 forecasting methods (2019)A comparison of multivariate and univariate time series approaches to modelling and forecasting emergency department demand in Western Australia (2015)Artificial Neural Network Based Approach for Blood Demand Forecasting (2017)Forecasting of BTC volatility: comparative study between parametric and nonparametric models (2019)Hospital daily outpatient visits forecasting using a combinatorial model based on ARIMA and SES models (2017)Retail Demand Forecasting: A Comparative Study for Multivariate Time Series (2023)Bagging exponential smoothing methods using STL decomposition and Box–Cox transformation (2016)The M4 Competition: Results, findings, conclusion and way forward (2018)M5 accuracy competition: Results, findings, and conclusions (2022)
this work references cited by second hop

References

  • 2008Automatic Time Series Forecasting: The forecast Package for R3,538 cited
  • 2000The M3-Competition: results, conclusions and implications1,700 cited
  • 2002A state space framework for automatic forecasting using exponential smoothing methods1,074 cited
  • 2009forecast: Forecasting Functions for Time Series and Linear Models791 cited
  • 2008Forecasting Daily Patient Volumes in the Emergency Department267 cited
  • 2007Forecasting emergency department presentations102 cited

Cited by

  • 2020Medical service demand forecasting using a hybrid model based on ARIMA and self-adaptive filtering method57 cited
  • 2019Forecasting of weekly patient visits to emergency department: real case study46 cited

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