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Machine learning applications in time series hierarchical forecasting
Mahdi Abolghasemi, Rob J Hyndman, Garth Tarr +1
Hierarchical forecasting (HF) is needed in many situations in the supply chain (SC) because managers often need different levels of forecasts at different levels of SC to make a de…
LSTM-MSNet: Leveraging Forecasts on Sets of Related Time Series with Multiple Seasonal Patterns
Kasun Bandara, Christoph Bergmeir, Hansika Hewamalage
Generating forecasts for time series with multiple seasonal cycles is an important use-case for many industries nowadays. Accounting for the multi-seasonal patterns becomes necessa…
Recurrent Neural Networks for Time Series Forecasting: Current Status and Future Directions
Hansika Hewamalage, Christoph Bergmeir, Kasun Bandara
Recurrent Neural Networks (RNN) have become competitive forecasting methods, as most notably shown in the winning method of the recent M4 competition. However, established statisti…
LoRMIkA: Local rule-based model interpretability with k-optimal associations
Dilini Rajapaksha, Christoph Bergmeir, Wray Buntine
As we rely more and more on machine learning models for real-life decision-making, being able to understand and trust the predictions becomes ever more important. Local explainer m…
Sales Demand Forecast in E-commerce using a Long Short-Term Memory Neural Network Methodology
Kasun Bandara, Peibei Shi, Christoph Bergmeir +3
Generating accurate and reliable sales forecasts is crucial in the E-commerce business. The current state-of-the-art techniques are typically univariate methods, which produce fore…