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Found 9 papers, 5 papers with code
Sales Demand Forecast in E-commerce using a Long Short-Term Memory Neural Network Methodology
Since the product assortment hierarchy in an E-commerce platform contains large numbers of related products, in which the sales demand patterns can be correlated, our attempt is to incorporate this cross-series information in a unified model.
Recurrent Neural Networks for Time Series Forecasting: Current Status and Future Directions
Recurrent Neural Networks (RNN) have become competitive forecasting methods, as most notably shown in the winning method of the recent M4 competition.
LSTM-MSNet: Leveraging Forecasts on Sets of Related Time Series with Multiple Seasonal Patterns
Generating forecasts for time series with multiple seasonal cycles is an important use-case for many industries nowadays.
Improving the Accuracy of Global Forecasting Models using Time Series Data Augmentation
Forecasting models that are trained across sets of many time series, known as Global Forecasting Models (GFM), have shown recently promising results in forecasting competitions and real-world applications, outperforming many state-of-the-art univariate forecasting techniques.
Global Models for Time Series Forecasting: A Simulation Study
Our experiments demonstrate that when trained as global forecasting models, techniques such as RNNs and LGBMs, which have complex non-linear modelling capabilities, are competitive methods in general under challenging forecasting scenarios such as series having short lengths, datasets with heterogeneous series and having minimal prior knowledge of the patterns of the series.
A Look at the Evaluation Setup of the M5 Forecasting Competition
Scale normalization of the M5 error measure results in less stability than other scale-free errors.
NeuralProphet: Explainable Forecasting at Scale
NeuralProphet is a hybrid forecasting framework based on PyTorch and trained with standard deep learning methods, making it easy for developers to extend the framework.
Forecast Evaluation for Data Scientists: Common Pitfalls and Best Practices
We elaborate on the different problematic characteristics of time series such as non-normalities and non-stationarities and how they are associated with common pitfalls in forecast evaluation.
The Energy Prediction Smart-Meter Dataset: Analysis of Previous Competitions and Beyond
These competitions focus on accurate energy consumption forecasting and the importance of interpretability in understanding the underlying factors.
