scholarly journals Multivariate Time Series Forecasting with Deep Learning Proceedings in Energy Consumption

2019 ◽  
Author(s):  
Nédra Mellouli ◽  
Mahdjouba Akerma ◽  
Minh Hoang ◽  
Denis Leducq ◽  
Anthony Delahaye
Keyword(s):  
Time Series ◽  
Deep Learning ◽  
Energy Consumption ◽  
Multivariate Time Series ◽  
Time Series Forecasting

scholarly journals Two Birds with One Stone: Series Saliency for Accurate and Interpretable Multivariate Time Series Forecasting

2021 ◽  
Author(s):  
Qingyi Pan ◽  
Wenbo Hu ◽  
Ning Chen
Keyword(s):  
Time Series ◽  
Deep Learning ◽  
Data Augmentation ◽  
Multivariate Time Series ◽  
Time Series Forecasting ◽  
Experimental Results ◽  
Learning Methods ◽  
Forecasting Accuracy ◽  
Sliding Windows ◽  
Scheme Of Series

It is important yet challenging to perform accurate and interpretable time series forecasting. Though deep learning methods can boost forecasting accuracy, they often sacrifice interpretability. In this paper, we present a new scheme of series saliency to boost both accuracy and interpretability. By extracting series images from sliding windows of the time series, we design series saliency as a mixup strategy with a learnable mask between the series images and their perturbed versions. Series saliency is model agnostic and performs as an adaptive data augmentation method for training deep models. Moreover, by slightly changing the objective, we optimize series saliency to find a mask for interpretable forecasting in both feature and time dimensions. Experimental results on several real datasets demonstrate that series saliency is effective to produce accurate time-series forecasting results as well as generate temporal interpretations.

scholarly journals Prediction of Electrical Energy Consumption Using LSTM Algorithm with Teacher Forcing Technique

2021 ◽  
Vol 4 (1) ◽  
pp. 90-95
Author(s):  
Sasmitoh Rahmad Riady ◽  
◽  
Tjong Wan Sen ◽  
Keyword(s):  
Time Series ◽  
Deep Learning ◽  
Energy Consumption ◽  
Multivariate Time Series ◽  
Electrical Energy ◽  
Activation Function ◽  
Time Series Model ◽  
Electrical Energy Consumption ◽  
Process Data ◽  
The Future

Electrical energy is an important foundation in world economic growth, therefore it requires an accurate prediction in predicting energy consumption in the future. The methods that are often used in previous research are the Time Series and Machine Learning methods, but recently there has been a new method that can predict energy consumption using the Deep Learning Method which can process data quickly for training and testing. In this research, the researcher proposes a model and algorithm which contained in Deep Learning, that is Multivariate Time Series Model with LSTM Algorithm and using Teacher Forcing Technique for predicting electrical energy consumption in the future. Because Multivariate Time Series Model and LSTM Algorithm can receive input with various conditions or seasons of electrical energy consumption. Teacher Forcing Technique is able lighten up the computation so that it can training and testing data quickly. The method used in this study is to compare Teacher Forcing LSTM with Non-Teacher Forcing LSTM in Multivariate Time Series model using several activation functions that produce significant differences. TF value of RMSE 0.006, MAE 0.070 and Non-TF has RMSE and MAE values of 0.117 and 0.246. The value of the two models is obtained from Sigmoid Activation and the worst value of the two models is in the Softmax activation function, with TF values is RMSE 0.423, MAE 0.485 and Non-TF RMSE 0.520, MAE 0.519.

scholarly journals Time Series Forecasting with Multi-Headed Attention-Based Deep Learning for Residential Energy Consumption

Energies ◽  
2020 ◽  
Vol 13 (18) ◽  
pp. 4722
Author(s):  
Seok-Jun Bu ◽  
Sung-Bae Cho
Keyword(s):  
Time Series ◽  
Deep Learning ◽  
Energy Consumption ◽  
Energy Demand ◽  
Multivariate Time Series ◽  
Learning Model ◽  
Residential Energy ◽  
Residential Energy Consumption ◽  
Sensor Signals ◽  
Deep Learning Model

Predicting residential energy consumption is tantamount to forecasting a multivariate time series. A specific window for several sensor signals can induce various features extracted to forecast the energy consumption by using a prediction model. However, it is still a challenging task because of irregular patterns inside including hidden correlations between power attributes. In order to extract the complicated irregular energy patterns and selectively learn the spatiotemporal features to reduce the translational variance between energy attributes, we propose a deep learning model based on the multi-headed attention with the convolutional recurrent neural network. It exploits the attention scores calculated with softmax and dot product operation in the network to model the transient and impulsive nature of energy demand. Experiments with the dataset of University of California, Irvine (UCI) household electric power consumption consisting of a total 2,075,259 time-series show that the proposed model reduces the prediction error by 31.01% compared to the state-of-the-art deep learning model. Especially, the multi-headed attention improves the prediction performance even more by up to 27.91% than the single-attention.

Deep Learning for text in limted data settings

2020 ◽  
Author(s):  
Pathikkumar Patel ◽  
Bhargav Lad ◽  
Jinan Fiaidhi
Keyword(s):  
Machine Learning ◽  
Time Series ◽  
Deep Learning ◽  
Sentiment Analysis ◽  
Transfer Learning ◽  
Text Classification ◽  
State Of The Art ◽  
Time Series Forecasting ◽  
Text Data ◽  
Performance Levels

During the last few years, RNN models have been extensively used and they have proven to be better for sequence and text data. RNNs have achieved state-of-the-art performance levels in several applications such as text classification, sequence to sequence modelling and time series forecasting. In this article we will review different Machine Learning and Deep Learning based approaches for text data and look at the results obtained from these methods. This work also explores the use of transfer learning in NLP and how it affects the performance of models on a specific application of sentiment analysis.

A C4.5 Fuzzy Decision Tree Method for Multivariate Time Series Forecasting

2021 ◽  
Author(s):  
Rafael R. C. Silva ◽  
Walmir Matos Caminhas ◽  
Petronio Candido de Lima e Silva ◽  
Frederico Gadelha Guimaraes
Keyword(s):  
Time Series ◽  
Decision Tree ◽  
Multivariate Time Series ◽  
Time Series Forecasting ◽  
Fuzzy Decision ◽  
Fuzzy Decision Tree ◽  
Decision Tree Method ◽  
Tree Method
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