  Multi-step prediction is considered of major significance for time series
analysis in many real life problems. Existing methods mainly focus on
one-step-ahead forecasting, since multiple step forecasting generally fails due
to accumulation of prediction errors. This paper presents a novel approach for
predicting plant growth in agriculture, focusing on prediction of plant Stem
Diameter Variations (SDV). The proposed approach consists of three main steps.
At first, wavelet decomposition is applied to the original data, as to
facilitate model fitting and reduce noise in them. Then an encoder-decoder
framework is developed using Long Short Term Memory (LSTM) and used for
appropriate feature extraction from the data. Finally, a recurrent neural
network including LSTM and an attention mechanism is proposed for modelling
long-term dependencies in the time series data. Experimental results are
presented which illustrate the good performance of the proposed approach and
that it significantly outperforms the existing models, in terms of error
criteria such as RMSE, MAE and MAPE.
