# Forecasting of Non-Stationary Sales Time Series Using Deep Learning

arXiv: https://arxiv.org/abs/2205.11636 · HTML: https://mlsanity.com/paper/2205.11636/

## Key facts

- Cited by 1 papers 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 (most cited first, 7 of 7)

- [PyTorch: An Imperative Style, High-Performance Deep Learning Library](https://mlsanity.com/paper/1912.01703/) (2019)
- [DeepAR: Probabilistic Forecasting with Autoregressive Recurrent Networks](https://mlsanity.com/paper/1704.04110/) (2017)
- [Temporal Fusion Transformers for Interpretable Multi-horizon Time Series Forecasting](https://mlsanity.com/paper/1912.09363/) (2019)
- [N-BEATS: Neural basis expansion analysis for interpretable time series forecasting](https://mlsanity.com/paper/1905.10437/) (2019)
- [Linear, Machine Learning and Probabilistic Approaches for Time Series Analysis](https://mlsanity.com/paper/1703.01977/) (2017)
- [Sales Time Series Analytics Using Deep Q-Learning](https://mlsanity.com/paper/2201.02058/) (2022)
- [Bayesian Regression Approach for Building and Stacking Predictive Models in Time Series Analytics](https://mlsanity.com/paper/2201.02034/) (2022)

## Built on by (most cited first, 1 of 1)

- [Analytics of Business Time Series Using Machine Learning and Bayesian Inference](https://mlsanity.com/paper/2205.12905/) (2022)

## 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.