Multivariate Time Series Forecasting Based Cloud Computing For Consumer Price Index Using Deep Learning Algorithms

2020 ◽  
Author(s):  
Soffa Zahara ◽  
Sugianto
Keyword(s):  
Time Series ◽  
Cloud Computing ◽  
Deep Learning ◽  
Price Index ◽  
Multivariate Time Series ◽  
Learning Algorithms ◽  
Consumer Price Index ◽  
Time Series Forecasting

Peramalan Data Indeks Harga Konsumen Berbasis Time Series Multivariate Menggunakan Deep Learning

2021 ◽  
Vol 5 (1) ◽  
pp. 24-30
Author(s):  
Soffa Zahara ◽  
Sugianto
Keyword(s):  
Time Series ◽  
Deep Learning ◽  
Multilayer Perceptron ◽  
Price Index ◽  
Multivariate Time Series ◽  
Consumer Price Index ◽  
New Approach ◽  
Development Method ◽  
Bidirectional Lstm ◽  
Addie Model

Multivariate Time Series based forecasting is a type of forecasting that has more than one criterion changes from time to time that it can forecast based on historical patterns of data sequences. The Consumer Price Index (CPI) issued regularly every month by the Statistics Indonesia calculated based on data observations. This study is a development of previous research that only used on type of algorithm to predict CPI value resulting poor of accuracy due to lack of architecture variations testing. This study developed a CPI forecasting model with a new approach about using several types of deep learning algorithms, namely LSTM, Bidirectional LSTM, and Multilayer Perceptron with architectural variations of the number of neurons and epochs. Furthermore, this study adapt ADDIE model of Research and Development method. Based on the results, the best accuracy is obtained from the LSTM Bidirectional with 10 neurons and 2000 epoch resulting 3,519 of RMSE value. Meanwhile, based on the average RMSE value for the whole test, LSTM gets the smallest average of RMSE followed Bidirectional LSTM and Multilayer Perceptron with the RMSE value 4,334, 5,630, 6,304 respectively.  

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 Estimation Models Generation using Linear Genetic Programming

2009 ◽  
Vol 12 (3) ◽  
Author(s):  
Javier Martínez Canillas ◽  
Roberto Sánchez ◽  
Benjamín Barán
Keyword(s):  
Time Series ◽  
Evolutionary Algorithms ◽  
Genetic Programming ◽  
Price Index ◽  
Consumer Price Index ◽  
Decision Rules ◽  
Time Series Forecasting ◽  
Linear Genetic Programming ◽  
Estimation Techniques ◽  
Estimation Models

The use of decision rules and estimation techniques is increasingly common for decision mak-ing. In recent years studies were conducted which applies Genetic Programming (GP) to obtainrules to make predictions. A new branch in the area of Evolutionary Algorithms (EA) is LinearGenetic Programming (LGP). LGP evolves instructions sequences of an imperative programminglanguage. This paper proposes estimation models generation for time series forecasting using LGP.The forecasting result for the Consumer Price Index (CPI) and the price of soybeans per ton showsthe potential of this new proposal.

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.

scholarly journals Forecasting Video QoE with Deep Learning from Multivariate Time-series

2021 ◽  
pp. 1-1
Author(s):  
Hossein Ebrahimidinaki ◽  
Shervin Shirmohammadi ◽  
Emil Janulewicz ◽  
David Cote
Keyword(s):  
Time Series ◽  
Deep Learning ◽  
Multivariate Time Series
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