Filtering harbor craft vessels’ fuel data using statistical, decomposition, and predictive methodologies
Filtering is the process of defining, recognizing, and correcting flaws in given data so that the influence of inaccuracies in input data on subsequent studies is minimized. This paper aims to discuss the characteristics of some filtering methods from various topics. Wavelet transform and frequency (Fourier) transform are considered for the decomposition methodologies whereas descriptive statistics is used for the statistical methodology. The Kalman filter and autoencoder neural network are also explored for the predictive methodologies. All the aforementioned methodologies are discussed empirically using two metrics operformance as compared to their counterparts in filtering out spikes found in the mass flow data.
Journal/Conference/Book titleMaritime Transport Research
- 6524 (R-MOE-A403-C002) Ship's Energy Efficiency via Machine Learning with Big Data Analytics