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Found 22 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.
Kernel Corrector LSTM
We empirically evaluate the forecasting accuracy and the training time of the new algorithm and compare it with cLSTM and LSTM.
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.
tsMorph: generation of semi-synthetic time series to understand algorithm performance
In this paper, we demonstrate the utility of tsMorph by assessing the performance of the Long Short-Term Memory Network forecasting algorithm.
Meta-aprendizado para otimizacao de parametros de redes neurais
In the current work, we performed a case study using meta-learning to choose the number of hidden nodes for MLP networks, which is an important parameter to be defined aiming a good networks performance.
Pastprop-RNN: improved predictions of the future by correcting the past
Forecasting accuracy is reliant on the quality of available past data.
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.
Promoting Fairness through Hyperparameter Optimization
Considerable research effort has been guided towards algorithmic fairness but real-world adoption of bias reduction techniques is still scarce.
u-cf2vec: Representation Learning for Personalized Algorithm Selection in Recommender Systems
In the meta level, the meta learning model will use the metafeatures to extract knowledge that will be used to predict the best algorithm for each user.
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.
A Bandit-Based Algorithm for Fairness-Aware Hyperparameter Optimization
Hence, coupled with the lack of tools for ML practitioners, real-world adoption of bias reduction methods is still scarce.
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.
Preference rules for label ranking: Mining patterns in multi-target relations
In CAR, the consequent is a single class, to which the example is expected to belong to.
Characterizing classification datasets: a study of meta-features for meta-learning
These characterizations, also called meta-features, describe properties of the data which are predictive for the performance of machine learning algorithms trained on them.
Algorithm Selection for Collaborative Filtering: the influence of graph metafeatures and multicriteria metatargets
However, the results have shown that the feature selection procedure used to create the comprehensive metafeatures is is not effective, since there is no gain in predictive performance.
Building robust prediction models for defective sensor data using Artificial Neural Networks
Predicting the health of components in complex dynamic systems such as an automobile poses numerous challenges.
Smart energy management as a means towards improved energy efficiency
The costs associated with refrigerator equipment often represent more than half of the total energy costs in supermarkets.
Learning Word Embeddings from the Portuguese Twitter Stream: A Study of some Practical Aspects
Using a single GPU, we were able to scale up vocabulary size from 2048 words embedded and 500K training examples to 32768 words over 10M training examples while keeping a stable validation loss and approximately linear trend on training time per epoch.
autoBagging: Learning to Rank Bagging Workflows with Metalearning
Automated machine learning (autoML) is the field of ML that attempts to answers these needs.
FEUP at SemEval-2017 Task 5: Predicting Sentiment Polarity and Intensity with Financial Word Embeddings
This paper presents the approach developed at the Faculty of Engineering of University of Porto, to participate in SemEval 2017, Task 5: Fine-grained Sentiment Analysis on Financial Microblogs and News.
