scholarly journals Forecasting the Carsharing Service Demand Using Uni and Multivariable Models

2020 ◽  
Author(s):  
Victor Aquiles Alencar ◽  
Lucas Ribeiro Pessamilio ◽  
Felipe Rooke da Silva ◽  
Heder Soares Bernardino ◽  
Alex Borges Vieira
Keyword(s):  
Short Term Memory ◽  
Meteorological Data ◽  
Absolute Error ◽  
Climatic Data ◽  
Short Term ◽  
Car Sharing ◽  
Urban Centers ◽  
Service Demand ◽  
Boosting Algorithms

Abstract Car-sharing is an alternative to urban mobility that has been widely adopted. However, this approach is prone to several problems, such as fleet imbalance, due to the variance of the daily demand in large urban centers. In this work, we apply two time series techniques, namely, Long Short-Term Memory (LSTM) and Prophet, to infer the demand for three real car-sharing services. We also apply several state-of-the-art models on free-floating data in order to get a better understanding of what works best for this type of data. In addition to historical data, we also use climatic attributes in LSTM applications. As a result, the addition of meteorological data improved the model’s performance, especially on Evo: an average Mean Absolute Error (MAE) of approximately 61.13 travels was obtained with the demand data on Evo, while MAE equals 32.72 travels was observed when adding the climatic data, the other datasets also improved but none other improved this much. For the free-floating data test, we got the Boosting Algorithms (XGBoost, Catboost, and LightGBM) got the best performance short term, the worst one has an improvement of around 22% of MAE over the next best-ranked (Prophet). Meanwhile in the long term Prophet got the best MAE result, around 22.5% better than the second-best (LSTM).

scholarly journals Forecasting the carsharing service demand using uni and multivariable models

2021 ◽  
Vol 12 (1) ◽  
Author(s):  
Victor Aquiles Alencar ◽  
Lucas Ribeiro Pessamilio ◽  
Felipe Rooke ◽  
Heder Soares Bernardino ◽  
Alex Borges Vieira
Keyword(s):  
Business Models ◽  
Short Term Memory ◽  
Mean Absolute Error ◽  
Meteorological Data ◽  
Absolute Error ◽  
Superior Performance ◽  
Climate Data ◽  
Short Term ◽  
Urban Centers ◽  
Service Demand

AbstractCarsharing is ana lternative to urban mobility that has been widely adopted recently. This service presents three main business models: two of these models base their services on stations while the remainder, the free-floating service, is free of fixed stations. Despite the notable advantages of carsharing, this service is prone to several problems, such as fleet imbalance due to the variance of the daily demand in large urban centers. Forecasting the demand for the service is a key task to deal with this issue. In this sense, in this work, we analyze the use of well-known techniques to forecast a carsharing service demand. More in deep, we evaluate the use of the Long Short-Term Memory (LSTM) and Prophet techniques to predict the demand of three real carsharing services. Moreover, we also evaluate seven state-of-the-art forecasting models on a given free-floating carsharing service, highlighting the potentials of each technique. In addition to historical carsharing service data, we have also used climatic series to enhance the forecasting. Indeed, the results of our analysis have shown that the addition of meteorological data improved the models’ performance. In this case, the mean absolute error of LSTM may fall by half, when using the climate data. When considering the free-floating carsharing service, and prediction for the short-term (i.e., 12 hours), the boosting algorithms (e.g. XGBoost, Catboost, and LightGBM) present superior performance, with less than 20% of mean absolute error when compared to the next best-ranked model (Prophet). On the other hand, Prophet performed better for predictions conducted on long-term periods.

scholarly journals Comparative Study of Univariate and Multivariate Long Short-Term Memory for Very Short-Term Forecasting of Global Horizontal Irradiance

Symmetry ◽  
2021 ◽  
Vol 13 (8) ◽  
pp. 1544
Author(s):  
Ashis Kumar Mandal ◽  
Rikta Sen ◽  
Saptarsi Goswami ◽  
Basabi Chakraborty
Keyword(s):  
Time Series ◽  
Wind Direction ◽  
Power Plants ◽  
Prediction Models ◽  
Short Term Memory ◽  
Meteorological Data ◽  
Absolute Error ◽  
Short Term ◽  
Term Memory ◽  
Long Short Term Memory

Accurate global horizontal irradiance (GHI) forecasting is crucial for efficient management and forecasting of the output power of photovoltaic power plants. However, developing a reliable GHI forecasting model is challenging because GHI varies over time, and its variation is affected by changes in weather patterns. Recently, the long short-term memory (LSTM) deep learning network has become a powerful tool for modeling complex time series problems. This work aims to develop and compare univariate and several multivariate LSTM models that can predict GHI in Guntur, India on a very short-term basis. To build the multivariate time series models, we considered all possible combinations of temperature, humidity, and wind direction variables along with GHI as inputs and developed seven multivariate models, while in the univariate model, we considered only GHI variability. We collected the meteorological data for Guntur from 1 January 2016 to 31 December 2016 and built 12 datasets, each containing variability of GHI, temperature, humidity, and wind direction of a month. We then constructed the models, each of which measures up to 2 h ahead of forecasting of GHI. Finally, to measure the symmetry among the models, we evaluated the performances of the prediction models using root mean square error (RMSE) and mean absolute error (MAE). The results indicate that, compared to the univariate method, each multivariate LSTM performs better in the very short-term GHI prediction task. Moreover, among the multivariate LSTM models, the model that incorporates the temperature variable with GHI as input has outweighed others, achieving average RMSE values 0.74 W/m2–1.5 W/m2.

scholarly journals The Prediction of Oceanic Mesoscale Eddy Properties and Propagation Trajectories Based on Machine Learning

Water ◽  
2020 ◽  
Vol 12 (9) ◽  
pp. 2521
Author(s):  
Xin Wang ◽  
Huizan Wang ◽  
Donghan Liu ◽  
Wenke Wang
Keyword(s):  
Machine Learning ◽  
Ocean Circulation ◽  
Prediction Models ◽  
Short Term Memory ◽  
Mesoscale Eddy ◽  
Absolute Error ◽  
Trajectory Data ◽  
Short Term ◽  
Prediction Ability

Mesoscale eddies play an important role in ocean circulation, material energy exchange and variation of ocean environments. Machine learning methods can efficiently process massive amounts of data and automatically learn the implicit features, thus providing a new approach to eddy prediction research. Using the mesoscale eddy trajectory data derived from multimission satellite altimetry, we propose relevant machine learning models based on long short-term memory network (LSTM) and the extra trees (ET) algorithm for the prediction of eddy properties and propagation trajectories. Characteristic factors, including attribute features and past eddy displacements, were exploited to construct prediction models with high effectiveness and few predictors. To study their effects at different forecasting times, we separately trained the models by rebuilding the corresponding relationship between eddy samples and labels. In addition, the variation characteristics and the predictability of eddy properties and propagation trajectories were discussed from the prediction results. Cross-validation shows that at different prediction times, our method is superior to previous methods in terms of the mean absolute error (MAE) of eddy properties and the root mean square error (RMSE) of propagation. The stable variation in eddy properties makes the prediction more dependent on the historical time series than that of a propagation forecast. The short-term propagation prediction of eddies contained more noise than long-term predictions, and the long-term predictions revealed a more significant trend. Finally, we discuss the effect of eddy properties on the prediction ability of the eddy propagation trajectory.

Conceptual short-term memory (CSTM) supports core claims of Christiansen and Chater

2016 ◽  
Vol 39 ◽  
Author(s):  
Mary C. Potter
Keyword(s):  
Working Memory ◽  
Rapid Serial Visual Presentation ◽  
Language Comprehension ◽  
Short Term Memory ◽  
Visual Presentation ◽  
Long Term Memory ◽  
Short Term ◽  
Term Memory ◽  
Use Of Knowledge

AbstractRapid serial visual presentation (RSVP) of words or pictured scenes provides evidence for a large-capacity conceptual short-term memory (CSTM) that momentarily provides rich associated material from long-term memory, permitting rapid chunking (Potter 1993; 2009; 2012). In perception of scenes as well as language comprehension, we make use of knowledge that briefly exceeds the supposed limits of working memory.

Comparison of Auditory, Language, Memory, and Attention Abilities in Children With and Without Listening Difficulties

2020 ◽  
Vol 29 (4) ◽  
pp. 710-727
Author(s):  
Beula M. Magimairaj ◽  
Naveen K. Nagaraj ◽  
Alexander V. Sergeev ◽  
Natalie J. Benafield
Keyword(s):  
Auditory Processing ◽  
Short Term Memory ◽  
Group Differences ◽  
Long Term Memory ◽  
Short Term ◽  
Significant Group ◽  
Term Memory ◽  
Memory And Attention ◽  
Speed And Accuracy

Objectives School-age children with and without parent-reported listening difficulties (LiD) were compared on auditory processing, language, memory, and attention abilities. The objective was to extend what is known so far in the literature about children with LiD by using multiple measures and selective novel measures across the above areas. Design Twenty-six children who were reported by their parents as having LiD and 26 age-matched typically developing children completed clinical tests of auditory processing and multiple measures of language, attention, and memory. All children had normal-range pure-tone hearing thresholds bilaterally. Group differences were examined. Results In addition to significantly poorer speech-perception-in-noise scores, children with LiD had reduced speed and accuracy of word retrieval from long-term memory, poorer short-term memory, sentence recall, and inferencing ability. Statistically significant group differences were of moderate effect size; however, standard test scores of children with LiD were not clinically poor. No statistically significant group differences were observed in attention, working memory capacity, vocabulary, and nonverbal IQ. Conclusions Mild signal-to-noise ratio loss, as reflected by the group mean of children with LiD, supported the children's functional listening problems. In addition, children's relative weakness in select areas of language performance, short-term memory, and long-term memory lexical retrieval speed and accuracy added to previous research on evidence-based areas that need to be evaluated in children with LiD who almost always have heterogenous profiles. Importantly, the functional difficulties faced by children with LiD in relation to their test results indicated, to some extent, that commonly used assessments may not be adequately capturing the children's listening challenges. Supplemental Material https://doi.org/10.23641/asha.12808607

scholarly journals A Novel LSTM Model with Interaction Dual Attention for Radar Echo Extrapolation

2021 ◽  
Vol 13 (2) ◽  
pp. 164
Author(s):  
Chuyao Luo ◽  
Xutao Li ◽  
Yongliang Wen ◽  
Yunming Ye ◽  
Xiaofeng Zhang
Keyword(s):  
Short Term Memory ◽  
Weather Forecast ◽  
Vital Role ◽  
Data Sets ◽  
Short Term ◽  
Learning Techniques ◽  
Radar Echo ◽  
Hidden States ◽  
Better Than

The task of precipitation nowcasting is significant in the operational weather forecast. The radar echo map extrapolation plays a vital role in this task. Recently, deep learning techniques such as Convolutional Recurrent Neural Network (ConvRNN) models have been designed to solve the task. These models, albeit performing much better than conventional optical flow based approaches, suffer from a common problem of underestimating the high echo value parts. The drawback is fatal to precipitation nowcasting, as the parts often lead to heavy rains that may cause natural disasters. In this paper, we propose a novel interaction dual attention long short-term memory (IDA-LSTM) model to address the drawback. In the method, an interaction framework is developed for the ConvRNN unit to fully exploit the short-term context information by constructing a serial of coupled convolutions on the input and hidden states. Moreover, a dual attention mechanism on channels and positions is developed to recall the forgotten information in the long term. Comprehensive experiments have been conducted on CIKM AnalytiCup 2017 data sets, and the results show the effectiveness of the IDA-LSTM in addressing the underestimation drawback. The extrapolation performance of IDA-LSTM is superior to that of the state-of-the-art methods.

scholarly journals Two Stage Continuous Gesture Recognition Based on Deep Learning

Electronics ◽  
2021 ◽  
Vol 10 (5) ◽  
pp. 534
Author(s):  
Huogen Wang
Keyword(s):  
Gesture Recognition ◽  
Large Scale ◽  
Short Term Memory ◽  
Short Term ◽  
Hand Motion ◽  
Spatiotemporal Features ◽  
Spatiotemporal Information ◽  
Video Frames ◽  
Depth Sequences

The paper proposes an effective continuous gesture recognition method, which includes two modules: segmentation and recognition. In the segmentation module, the video frames are divided into gesture frames and transitional frames by using the information of hand motion and appearance, and continuous gesture sequences are segmented into isolated sequences. In the recognition module, our method exploits the spatiotemporal information embedded in RGB and depth sequences. For the RGB modality, our method adopts Convolutional Long Short-Term Memory Networks to learn long-term spatiotemporal features from short-term spatiotemporal features obtained from a 3D convolutional neural network. For the depth modality, our method converts a sequence into Dynamic Images and Motion Dynamic Images through weighted rank pooling and feed them into Convolutional Neural Networks, respectively. Our method has been evaluated on both ChaLearn LAP Large-scale Continuous Gesture Dataset and Montalbano Gesture Dataset and achieved state-of-the-art performance.

scholarly journals An Approach toward the Development of a Functional Encoding Model of Short Term Memory during Reading

1978 ◽  
Vol 10 (2) ◽  
pp. 141-148
Author(s):  
Mary Anne Herndon
Keyword(s):  
Cluster Analysis ◽  
Storage Capacity ◽  
Short Term Memory ◽  
Long Term Memory ◽  
Short Term ◽  
Processing Efficiency ◽  
Term Memory ◽  
Information Unit ◽  
Subsequent Storage

In a model of the functioning of short term memory, the encoding of information for subsequent storage in long term memory is simulated. In the encoding process, semantically equivalent paragraphs are detected for recombination into a macro information unit. This recombination process can be used to relieve the limited storage capacity constraint of short term memory and subsequently increase processing efficiency. The results of the simulation give a favorable indication of the success for the use of cluster analysis as a tool to simulate the encoding function in the detection of semantically similar paragraphs.

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