SZodixStudyAcademic Library
ShelvesOpen access
Study · an open academic library·Metadata from OpenAlex (CC0)·Shelves·OpenAlex metadata, de-duplicated
Forecasting Brazilian mortality rates due to occupational accidents using autoregressive moving average approaches
16 references·0 citing·35 second hop
Forecasting Brazilian mortality rates due to occupational accidents using autoregressive moving average approaches (2020)Forecasting Brazilian mortality rates due…The inventory performance of forecasting methods: Evidence from the M3 competition data (2018)The inventory performance of …Modeling and forecasting call center arrivals: A literature survey and a case study (2016)Modeling and forecasting call…Introduction to Time Series and Forecasting (2016)Introduction to Time Series a…Grouped Functional Time Series Forecasting: An Application to Age-Specific Mortality Rates (2016)Grouped Functional Time Serie…Gender and the labor market: What have we learned from field and lab experiments? (2014)Gender and the labor market: …Testing for unit roots in bounded time series (2013)Testing for unit roots in bou…forecast: Forecasting Functions for Time Series and Linear Models (2009)forecast: Forecasting Functio…Time series count data models: An empirical application to traffic accidents (2008)Time series count data models…25 years of time series forecasting (2006)25 years of time series forec…Introduction to Time Series and Forecasting (2002)Introduction to Time Series a…Numerical distribution functions for unit root and cointegration tests (1996)Numerical distribution functi…Testing the null hypothesis of stationarity against the alternative of a unit root (1992)Testing the null hypothesis o…Tests for Unit Roots: A Monte Carlo Investigation (1989)Tests for Unit Roots: A Monte…Tests For Unit Roots: A Monte Carlo Investigation (1988)Tests For Unit Roots: A Monte…Time Series Analysis: Forecasting and Control (1977)Time Series Analysis: Forecas…Forecasting principles and practiceForecasting principles and pr…Distribution of the Estimators for Autoregressive Time Series with a Unit Root (1979)Distribution of the Estimators for Autoregressive Time Series With a Unit Root (1979)Testing for a Unit Root in Time Series Regression (1988)A Comparative Study of Unit Root Tests with Panel Data and a New Simple Test (1999)Unit root tests for panel data (2001)Modelling Asymmetric Cointegration and Dynamic Multipliers in a Nonlinear ARDL Framework (2014)Co-Integration and Error Correction: Representation, Estimation, and Testing (1987)Numerical distribution functions of likelihood ratio tests for cointegration (1999)Critical Values for Cointegration Tests (2010)Autoregressive Conditional Heteroscedasticity with Estimates of the Variance of United Kingdom Inflation (1982)On the use of cross-validation for time series predictor evaluation (2012)Stock price prediction using LSTM, RNN and CNN-sliding window model (2017)Time series forecasting of petroleum production using deep LSTM recurrent networks (2018)Generalized autoregressive conditional heteroskedasticity (1986)Analysis of Financial Time Series (2006)Cross‐validation strategies for data with temporal, spatial, hierarchical, or phylogenetic structure (2016)Financial time series forecasting model based on CEEMDAN and LSTM (2018)Automatic Time Series Forecasting: The forecast Package for R (2008)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)Likelihood Ratio Statistics for Autoregressive Time Series with a Unit Root (1981)The Dividend-Price Ratio and Expectations of Future Dividends and Discount Factors (1986)Determining the Number of Factors in Approximate Factor Models (2002)A PANIC Attack on Unit Roots and Cointegration (2004)American Economic Review (2019)A Comparison of ARIMA and LSTM in Forecasting Time Series (2018)The Performance of LSTM and BiLSTM in Forecasting Time Series (2019)Recurrent Neural Networks for Time Series Forecasting: Current status and future directions (2020)The Dividend-Price Ratio and Expectations of Future Dividends and Discount Factors (1988)Why Does Stock Market Volatility Change Over Time? (1989)Unit root tests in panel data: asymptotic and finite-sample properties (2002)A review on singular spectrum analysis for economic and financial time series (2010)SutteARIMA: Short-term forecasting method, a case: Covid-19 and stock market in Spain (2020)An Advanced CNN-LSTM Model for Cryptocurrency Forecasting (2021)
this work references cited by second hop

References

  • 1977Time Series Analysis: Forecasting and Control19,200 cited
  • 1992Testing the null hypothesis of stationarity against the alternative of a unit root12,634 cited
  • n.d.Forecasting principles and practice4,364 cited
  • 2002Introduction to Time Series and Forecasting3,913 cited
  • 1996Numerical distribution functions for unit root and cointegration tests3,119 cited
  • 200625 years of time series forecasting1,589 cited
  • 1988Tests For Unit Roots: A Monte Carlo Investigation1,244 cited
  • 2016Introduction to Time Series and Forecasting1,190 cited
  • 2009forecast: Forecasting Functions for Time Series and Linear Models791 cited
  • 1989Tests for Unit Roots: A Monte Carlo Investigation635 cited
  • 2014Gender and the labor market: What have we learned from field and lab experiments?314 cited
  • 2008Time series count data models: An empirical application to traffic accidents261 cited
  • 2013Testing for unit roots in bounded time series95 cited
  • 2016Modeling and forecasting call center arrivals: A literature survey and a case study95 cited
  • 2016Grouped Functional Time Series Forecasting: An Application to Age-Specific Mortality Rates88 cited

Only edges whose both ends are imported exist here. Widen the year window, or show the whole map. Back to the work

  • 2018The inventory performance of forecasting methods: Evidence from the M3 competition data70 cited
  • Cited by

    No citing works inside the corpus for this window.