The Dejavu paper is accepted in the Journal of Business Research

Our Dejavu paper is accepted in the Journal of Business Research.

Yanfei Kang, Evangelos Spiliotis, Fotios Petropoulos, Nikolaos Athiniotis, Feng Li, Vassilios Assimakopoulo (2020). Déjà vu: A data-centric forecasting approach through time series cross-similarity, Journal of Business Research. (In Press)

[Working Paper | Software]

Accurate forecasts are vital for supporting the decisions of modern companies. Forecasters typically select the most appropriate statistical model for each time series. However, statistical models usually presume some data generation process while making strong assumptions about the errors. In this paper, we present a novel data-centric approach — “forecasting with similarity”, which tackles model uncertainty in a model-free manner. Existing similarity-based methods focus on identifying similar patterns within the series, i.e., “self-similarity”. In contrast, we propose searching for similar patterns from a reference set, i.e., “cross-similarity”. Instead of extrapolating, the future paths of the similar series are aggregated to obtain the forecasts of the target series. Building on the cross-learning concept, our approach allows the application of similarity-based forecasting on series with limited lengths. We evaluate the approach using a rich collection of real data and show that it yields competitive accuracy in both points forecasts and prediction intervals.


The DeepTCN paper is accepted in Neurocomputing

The DeepTCN paper is accepted in Neurocomputing.

Yitian Chen, Yanfei Kang, Yixiong Chen and Zizhuo Wang. (2020). Probabilistic Forecasting with Temporal Convolutional Neural Network. Neurocomputing 399C (2020) pp. 491-501.

[ Online | Working PaperSoftware]

We present a probabilistic forecasting framework based on convolutional neural network (CNN) for multiple related time series forecasting. The framework can be applied to estimate prob- ability density under both parametric and non-parametric settings. More specifically, stacked residual blocks based on dilated causal convolutional nets are constructed to capture the temporal dependencies of the series. Combined with representation learning, our approach is able to learn complex patterns such as seasonality, holiday effects within and across series, and to leverage those patterns for more accurate forecasts, especially when historical data is sparse or unavailable. Extensive empirical studies are performed on several real-world datasets, including datasets from, China’s largest online retailer. The results show that our framework compares favorably to the state-of-the-art in both point and probabilistic forecasting.