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Found 14 papers, 10 papers with code
Time Series Data Augmentation as an Imbalanced Learning Problem
We use these techniques to create synthetic time series observations and improve the accuracy of forecasting models.
On-the-fly Data Augmentation for Forecasting with Deep Learning
We validated the proposed approach using a state-of-the-art deep learning forecasting method and 8 benchmark datasets containing a total of 75797 time series.
Multi-output Ensembles for Multi-step Forecasting
On the other hand, the literature regarding the application of dynamic ensembles for multi-step ahead forecasting is scarce.
Exceedance Probability Forecasting via Regression for Significant Wave Height Prediction
This task is framed as an exceedance probability forecasting problem.
Automated Imbalanced Classification via Layered Learning
The main contribution of our work is a new method called ICLL for tackling IBC tasks which is not based on resampling training observations.
AutoFITS: Automatic Feature Engineering for Irregular Time Series
We hypothesise that, in irregular time series, the time at which each observation is collected may be helpful to summarise the dynamics of the data and improve forecasting performance.
Model Compression for Dynamic Forecast Combination
In this paper, we leverage the idea of model compression to address this problem in time series forecasting tasks.
Model Selection for Time Series Forecasting: Empirical Analysis of Different Estimators
We address this issue and compare a set of estimation methods for model selection in time series forecasting tasks.
STUDD: A Student-Teacher Method for Unsupervised Concept Drift Detection
In a set of experiments using 19 data streams, we show that the proposed approach can detect concept drift and present a competitive behaviour relative to the state of the art approaches.
Early Anomaly Detection in Time Series: A Hierarchical Approach for Predicting Critical Health Episodes
The early detection of anomalous events in time series data is essential in many domains of application.
VEST: Automatic Feature Engineering for Forecasting
Time series forecasting is a challenging task with applications in a wide range of domains.
Machine Learning vs Statistical Methods for Time Series Forecasting: Size Matters
Using a learning curve method, our results suggest that machine learning methods improve their relative predictive performance as the sample size grows.
Evaluating time series forecasting models: An empirical study on performance estimation methods
In this paper we address the application of these methods to time series forecasting tasks.
How to evaluate sentiment classifiers for Twitter time-ordered data?
Sentiment classes are ordered and unbalanced, and Twitter produces a stream of time-ordered data.
