Predicting the Future is Like Completing a Painting: Towards a Novel Method for Time-Series Forecasting

@article{Maaroufi2020PredictingTF,
  title={Predicting the Future is Like Completing a Painting: Towards a Novel Method for Time-Series Forecasting},
  author={Nadir Maaroufi and Mehdi Najib and Mohamed Bakhouya},
  journal={IEEE Access},
  year={2020},
  volume={9},
  pages={119918-119938}
}
This article is an introductory work towards a larger research framework relative to Scientific Prediction. It is a mixed between science and philosophy of science, therefore we can talk about Experimental Philosophy of Science. As a first result, we introduce a new forecasting method based on image completion, named Forecasting Method by Image Inpainting (FM2I). In fact, time series forecasting is transformed into fully images- and signal-based processing procedures. After transforming a time… 

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References

SHOWING 1-10 OF 90 REFERENCES

Forecasting with time series imaging

Imaging Time-Series to Improve Classification and Imputation

This work proposes a novel framework for encoding time series as different types of images, namely, Gramian Angular Summation/Difference Fields (GASF/GADF) and Markov Transition Fields (MTF), which enables the use of techniques from computer vision for time series classification and imputation.

A deep learning framework for time series classification using Relative Position Matrix and Convolutional Neural Network

Image inpainting

A novel algorithm for digital inpainting of still images that attempts to replicate the basic techniques used by professional restorators, and does not require the user to specify where the novel information comes from.

Sensor Classification Using Convolutional Neural Network by Encoding Multivariate Time Series as Two-Dimensional Colored Images

The proposed framework encodes multivariate time series data into two-dimensional colored images, and concatenate the images into one bigger image for classification through a Convolutional Neural Network (ConvNet).

Everything is Image: CNN-based Short-Term Electrical Load Forecasting for Smart Grid

  • Liangzhi LiK. OtaM. Dong
  • Engineering, Computer Science
    2017 14th International Symposium on Pervasive Systems, Algorithms and Networks & 2017 11th International Conference on Frontier of Computer Science and Technology & 2017 Third International Symposium of Creative Computing (ISPAN-FCST-ISCC)
  • 2017
A novel deep learning based short-term forecasting (DLSF) method that can perform accurate clustering on the input data using a deep Convolutional Neural Network model and another neural network with three hiddenlayers is used to predict the electric load.

Image Inpainting by Patch Propagation Using Patch Sparsity

A novel examplar-based inpainting algorithm through investigating the sparsity of natural image patches that enables better discrimination of structure and texture, and the patch sparse representation forces the newly inpainted regions to be sharp and consistent with the surrounding textures.

Machine learning for time series forecasting - a simulation study

Assessment of popular machine learning algorithms for time series prediction tasks reveals that advanced machine learning models are capable of approximating the optimal forecast very closely in the base case, with nonlinear models in the lead across all DGPs - particularly the MLP.
...