Forecasting of Non-Stationary Sales Time Series Using Deep Learning
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Abstract (arXiv)
The paper describes the deep learning approach for forecasting non-stationary time series with using time trend correction in a neural network model. Along with the layers for predicting sales values, the neural network model includes a subnetwork block for the prediction weight for a time trend term which is added to a predicted sales value. The time trend term is considered as a product of the predicted weight value and normalized time value. The results show that the forecasting accuracy can be essentially improved for non-stationary sales with time trends using the trend correction block in the deep learning model.
Key facts. Cited by 1 paper in the map (data through 14 Oct 2026). Builds on 7 of its 38 references that are in the map. First posted on arXiv 23 May 2022.
Builds on
7 of 38 references.
References · 7
- 2019PyTorch: An Imperative Style, High-Performance Deep Learning Librarycited by 20,305
- 2017DeepAR: Probabilistic Forecasting with Autoregressive Recurrent Networkscited by 1,013
- 2019Temporal Fusion Transformers for Interpretable Multi-horizon Time Series Forecastingcited by 761
- 2019N-BEATS: Neural basis expansion analysis for interpretable time series forecastingcited by 753
- 2017Linear, Machine Learning and Probabilistic Approaches for Time Series Analysiscited by 5
- 2022Sales Time Series Analytics Using Deep Q-Learningcited by 3
- 2022Bayesian Regression Approach for Building and Stacking Predictive Models in Time Series Analyticscited by 2
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Citing papers per year: 2022: 1.
1 paper.