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Found 11 papers, 6 papers with code
Extreme Value Modelling of Feature Residuals for Anomaly Detection in Dynamic Graphs
Detecting anomalies in a temporal sequence of graphs can be applied is areas such as the detection of accidents in transport networks and cyber attacks in computer networks.
Monash Time Series Forecasting Archive
Many businesses and industries nowadays rely on large quantities of time series data making time series forecasting an important research area.
Principles and Algorithms for Forecasting Groups of Time Series: Locality and Globality
In particular, global linear models can provide competitive accuracy with two orders of magnitude fewer parameters than local methods.
Distributed ARIMA Models for Ultra-long Time Series
Providing forecasts for ultra-long time series plays a vital role in various activities, such as investment decisions, industrial production arrangements, and farm management.
Applications Computation
Hierarchical forecast reconciliation with machine learning
First, the proposed method allows for a non-linear combination of the base forecasts, thus being more general than the linear approaches.
Machine learning applications in time series hierarchical forecasting
We perform an in-depth analysis of 61 groups of time series with different volatilities and show that ML models are competitive and outperform some well-established models in the literature.
Anomaly Detection in High Dimensional Data
The HDoutliers algorithm is a powerful unsupervised algorithm for detecting anomalies in high-dimensional data, with a strong theoretical foundation.
GRATIS: GeneRAting TIme Series with diverse and controllable characteristics
The explosion of time series data in recent years has brought a flourish of new time series analysis methods, for forecasting, clustering, classification and other tasks.
Coherent probabilistic forecasts for hierarchical time series
Many applications require forecasts for a hierarchy comprising a set of time series along with aggregates of subsets of these series.
Forecasting Time Series With Complex Seasonal Patterns Using Exponential Smoothing
An innovations state space modeling framework is introduced for forecasting complex seasonal time series such as those with multiple seasonal periods, high-frequency seasonality, non-integer seasonality, and dual-calendar effects.
The tourism forecasting competition
We find that pure time series approaches provide more accurate forecasts for tourism data than models with explanatory variables.
