Name
Title | ||
|---|---|---|
A Flexible Forecasting Framework for Hierarchical Time Series with Seasonal Patterns: A Case Study of Web Traffic. |
Abstract | ||
|---|---|---|
In this work, we focus on models and analysis of multivariate time series data that are organized in hierarchies. Such time series are referred to as hierarchical time series (HTS) and they are very common in business, management, energy consumption, social networks, or web traffic modeling and analysis. We propose a new flexible hierarchical forecasting framework, that takes advantage of the hierarchical relational structure to predict individual time series. Our new forecasting framework is able to (1) handle HTS modeling and forecasting problems; (2) make accurate forecasting for HTS with seasonal patterns; (3) incorporate various individual forecasting models and combine heuristics based on the HTS datasets' own characterization. The proposed framework is evaluated on a real-world web traffic data set. The results demonstrate that our approach is superior when applied to hierarchical web traffic prediction problems, and it outperforms alternative time series prediction models in terms of accuracy
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Year | DOI | Venue |
|---|---|---|
2018 | 10.1145/3209978.3210069 | SIGIR |
Keywords | Field | DocType |
Multivariate time series,Forecasting | Web traffic,Data mining,Time series,Social network,Multivariate statistics,Computer science,Relational structure,Heuristics,Hierarchy,Energy consumption | Conference |
ISBN | Citations | PageRank |
978-1-4503-5657-2 | 0 | 0.34 |
References | Authors | |
2 | 3 | |
Name | Order | Citations | PageRank |
|---|---|---|---|
| Zitao Liu | 1 | 166 | 25.49 |
| Yan Yan | 2 | 691 | 31.13 |
| Milos Hauskrecht | 3 | 921 | 90.70 |