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Forecasting seasonal demand for retail: A Fourier time-varying grey model
16 references·1 citing·27 second hop
Forecasting seasonal demand for retail: A Fourier time-varying grey model (2024)Forecasting seasonal demand for retail: A…M5 accuracy competition: Results, findings, and conclusions (2022)M5 accuracy competition: Resu…Forecasting: theory and practice (2022)Forecasting: theory and pract…An optimized model using LSTM network for demand forecasting (2020)An optimized model using LSTM…Retail forecasting: Research and practice (2019)Retail forecasting: Research …A seasonal GM(1,1) model for forecasting the electricity consumption of the primary economic sectors (2018)A seasonal GM(1,1) model for …Neural network ensemble operators for time series forecasting (2014)Neural network ensemble opera…Measuring forecasting accuracy: The case of judgmental adjustments to SKU-level demand forecasts (2013)Measuring forecasting accurac…Forecasting Time Series With Complex Seasonal Patterns Using Exponential Smoothing (2011)Forecasting Time Series With …Forecasting monthly and quarterly time series using STL decomposition (2011)Forecasting monthly and quart…Forecasting with Exponential Smoothing : The State Space Approach (2008)Forecasting with Exponential …Automatic Time Series Forecasting: The forecast Package for R (2008)Automatic Time Series Forecas…Another look at measures of forecast accuracy (2006)Another look at measures of f…Forecasting seasonals and trends by exponentially weighted moving averages (2004)Forecasting seasonals and tre…Forecasting with artificial neural networks: (1998)Forecasting with artificial n…Forecasting Sales by Exponentially Weighted Moving Averages (1960)Forecasting Sales by Exponent…Forecasting principles and practiceForecasting principles and pr…A novel attLSTM framework combining the attention mechanism and bidirectional LSTM for demand forecasting (2024)A novel attLSTM framework com…Time Series Analysis: Forecasting and Control (1977)Autoregressive Conditional Heteroscedasticity with Estimates of the Variance of United Kingdom Inflation (1982)Managerial Applications of Neural Networks: The Case of Bank Failure Predictions (1992)Financial time series forecasting using support vector machines (2003)Time series forecasting using a hybrid ARIMA and neural network model (2003)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)Fuzzy sets (1965)Distribution of the Estimators for Autoregressive Time Series with a Unit Root (1979)User Acceptance of Computer Technology: A Comparison of Two Theoretical Models (1989)Deep Learning for Time Series Forecasting: Tutorial and Literature Survey (2022)Forecast combinations: An over 50-year review (2022)Forecast evaluation for data scientists: common pitfalls and best practices (2022)DeepAR: Probabilistic forecasting with autoregressive recurrent networks (2019)The M5 competition: Background, organization, and implementation (2021)Statistical and Machine Learning forecasting methods: Concerns and ways forward (2018)Recurrent Neural Networks for Time Series Forecasting: Current status and future directions (2020)25 years of time series forecasting (2006)Exponential smoothing: The state of the art—Part II (2006)A new metric of absolute percentage error for intermittent demand forecasts (2016)Forecasting Destination Weekly Hotel Occupancy with Big Data (2016)Structural combination of seasonal exponential smoothing forecasts applied to load forecasting (2018)Exponential smoothing: The state of the art (1985)A Comparison of ARIMA and LSTM in Forecasting Time Series (2018)The Performance of LSTM and BiLSTM in Forecasting Time Series (2019)A note on the validity of cross-validation for evaluating autoregressive time series prediction (2017)A hybrid method of exponential smoothing and recurrent neural networks for time series forecasting (2019)
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References

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  • n.d.Forecasting principles and practice4,364 cited
  • 1998Forecasting with artificial neural networks:4,225 cited
  • 2008Automatic Time Series Forecasting: The forecast Package for R3,538 cited
  • 1960Forecasting Sales by Exponentially Weighted Moving Averages2,117 cited
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  • 2008Forecasting with Exponential Smoothing : The State Space Approach818 cited
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  • 2014Neural network ensemble operators for time series forecasting292 cited
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  • 2011Forecasting monthly and quarterly time series using STL decomposition226 cited

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  • 2013Measuring forecasting accuracy: The case of judgmental adjustments to SKU-level demand forecasts193 cited
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    • 2024A novel attLSTM framework combining the attention mechanism and bidirectional LSTM for demand forecasting44 cited