Основні поняття
Kernel Corrector LSTM (KcLSTM) is a computationally efficient variant of the Corrector LSTM (cLSTM) algorithm that improves time series forecasting by dynamically correcting the training data during the learning process.
Анотація
The paper introduces Kernel Corrector LSTM (KcLSTM), a new algorithm for time series forecasting that aims to improve upon the Corrector LSTM (cLSTM) approach.
Key highlights:
- Traditional machine learning models are often "read-only", unable to correct the training data during the learning process. The concept of Read-Write Machine Learning (RW-ML) addresses this limitation.
- cLSTM is an RW-ML algorithm that dynamically adjusts the training data to improve forecasting accuracy, but it is computationally expensive due to its use of a meta-learner.
- KcLSTM replaces the meta-learner in cLSTM with a simpler Kernel Smoothing method to detect and correct anomalies in the training data.
- Empirical evaluation shows that KcLSTM achieves better forecasting accuracy than LSTM and cLSTM, while also being faster than cLSTM, although the computational efficiency improvement is not as substantial as expected.
- The data correction in KcLSTM is more sensitive to the training data, and it can sometimes worsen the predictions if the data does not contain clear anomalies.
Overall, the paper demonstrates the potential of RW-ML approaches like KcLSTM to improve time series forecasting by dynamically correcting the training data, while also highlighting the challenges in balancing computational efficiency and forecasting performance.
Статистика
The average length of the monthly time series in the dataset is 366.
The mean value of the time series is 4222, with a standard deviation of 1160.
Цитати
"Traditional machine learning (ML) models are often considered read-only models, capable of learning from data but neglecting the feedback loop for correcting the data during the learning process."
"cLSTM has demonstrated superior predictive performance compared to traditional LSTM models. However, the computational cost associated with the meta-learning component of cLSTM is significant."
"Results reveal that KcLSTM achieves better predictive performance than LSTM and cLSTM, while also being faster than cLSTM, although the computational efficiency improvement is not as substantial as expected."