Time series prediction is a problem with a wide range of applications, including energy systems planning, currency forecasting, stock exchange operations or traffic prediction. Accordingly, a number of different prediction approaches have been proposed such as linear models, Feedforward Neural network models, Recurrent Neural networks or Fuzzy Neural Models. In this paper one presents a predict...

This paper presents a time series forecasting methodology and applies it to generate multiple–step–ahead predictions for the direction of change of the daily exchange rate of the Japanese Yen against the US Dollar. The proposed methodology draws from the disciplines of chaotic time series analysis, clustering, and artificial neural networks. In brief, clustering is applied to identify neighborh...

Despite the superiority of convolutional neural networks demonstrated in time series modeling and forecasting, it has not been fully explored on design network architecture tuning hyper-parameters. Inspired by incremental construction strategy for building a random multilayer perceptron, we propose novel Error-feedback Stochastic Modeling (ESM) to construct Convolutional Neural Network (ESM-CNN...

It is well known that coalmine gas concentration forecasting is very significant to ensure the safety of mining. Owing to the high-frequency, non-stationary, fluctuations, and chaotic properties of the gas concentration time series, a gas concentration forecasting model utilizing the original raw data often leads to an inability to provide satisfying forecast results. A hybrid forecasting model...

Multi-step prediction is a difficult task ¡hat has been attracted increasing tbe inieres! in recen! years. It tries to achieve predictions several sleps ahead ¡nto the tuture starting from information al time k. This paper is facllsed on the dcvelopment oí nonlinear neural models with tbe purpose oí building long-teTm Uf multi-step time series prediction schemes. In these context, the mos! popu...

Rainfall–runoff modeling is a complex hydrological issue that still has room for improvement. This study developed coupled bidirectional long short-term memory (LSTM) with sequence-to-sequence (Seq2Seq) learning (BiLSTM-Seq2seq) model to simulate multi-step-ahead runoff flood events. The LSTM Seq2Seq (LSTM-Seq2Seq) and multilayer perceptron (MLP) was set as benchmarks. results show that: (1) ro...

The framework of online learning with memory naturally captures learning problems with temporal effects, and was previously studied for the experts setting. In this work we extend the notion of learning with memory to the general Online Convex Optimization (OCO) framework, and present two algorithms that attain low regret. The first algorithm applies to Lipschitz continuous loss functions, obta...