Search Results for author: Hansika Hewamalage

Found 8 papers, 5 papers with code

Forecast Evaluation for Data Scientists: Common Pitfalls and Best Practices

no code implementations21 Mar 2022 Hansika Hewamalage, Klaus Ackermann, Christoph Bergmeir

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.

Decision Making Time Series

NeuralProphet: Explainable Forecasting at Scale

1 code implementation29 Nov 2021 Oskar Triebe, Hansika Hewamalage, Polina Pilyugina, Nikolay Laptev, Christoph Bergmeir, Ram Rajagopal

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.

Decision Making Time Series

A Look at the Evaluation Setup of the M5 Forecasting Competition

no code implementations8 Aug 2021 Hansika Hewamalage, Pablo Montero-Manso, Christoph Bergmeir, Rob J Hyndman

Scale normalization of the M5 error measure results in less stability than other scale-free errors.

Decision Making

Global Models for Time Series Forecasting: A Simulation Study

1 code implementation23 Dec 2020 Hansika Hewamalage, Christoph Bergmeir, Kasun Bandara

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.

Time Series Time Series Forecasting

Improving the Accuracy of Global Forecasting Models using Time Series Data Augmentation

no code implementations6 Aug 2020 Kasun Bandara, Hansika Hewamalage, Yuan-Hao Liu, Yanfei Kang, Christoph Bergmeir

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.

Data Augmentation Dynamic Time Warping +2

LSTM-MSNet: Leveraging Forecasts on Sets of Related Time Series with Multiple Seasonal Patterns

3 code implementations10 Sep 2019 Kasun Bandara, Christoph Bergmeir, Hansika Hewamalage

Generating forecasts for time series with multiple seasonal cycles is an important use-case for many industries nowadays.

Time Series

Recurrent Neural Networks for Time Series Forecasting: Current Status and Future Directions

3 code implementations2 Sep 2019 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.

Time Series Time Series Forecasting

Sales Demand Forecast in E-commerce using a Long Short-Term Memory Neural Network Methodology

1 code implementation13 Jan 2019 Kasun Bandara, Peibei Shi, Christoph Bergmeir, Hansika Hewamalage, Quoc Tran, Brian Seaman

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.

Time Series

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