scholarly journals Long-Short Term Memory for an Effective Short-Term Weather Forecasting Model Using Surface Weather Data

2019 ◽  
pp. 382-390 ◽  
Author(s):  
Pradeep Hewage ◽  
Ardhendu Behera ◽  
Marcello Trovati ◽  
Ella Pereira
Keyword(s):  
Short Term Memory ◽  
Weather Forecasting ◽  
Weather Data ◽  
Forecasting Model ◽  
Short Term ◽  
Term Memory ◽  
Surface Weather ◽  
Long Short Term Memory

The importance of short lag-time in the runoff forecasting model based on long short-term memory

2020 ◽  
Vol 589 ◽  
pp. 125359
Author(s):  
Xi Chen ◽  
Jiaxu Huang ◽  
Zhen Han ◽  
Hongkai Gao ◽  
Min Liu ◽  
...  
Keyword(s):  
Short Term Memory ◽  
Lag Time ◽  
Forecasting Model ◽  
Short Term ◽  
Term Memory ◽  
Model Based ◽  
Runoff Forecasting ◽  
Long Short Term Memory

scholarly journals Empirical mode decomposition based long short-term memory neural network forecasting model for the short-term metro passenger flow

PLoS ONE ◽  
2019 ◽  
Vol 14 (9) ◽  
pp. e0222365 ◽  
Author(s):  
Quanchao Chen ◽  
Di Wen ◽  
Xuqiang Li ◽  
Dingjun Chen ◽  
Hongxia Lv ◽  
...  
Keyword(s):  
Neural Network ◽  
Empirical Mode Decomposition ◽  
Short Term Memory ◽  
Forecasting Model ◽  
Short Term ◽  
Term Memory ◽  
Passenger Flow ◽  
Mode Decomposition ◽  
Long Short Term Memory

Deep Network based on Long Short-Term Memory for Time Series Prediction of Microclimate Data inside the Greenhouse

2020 ◽  
Vol 19 (02) ◽  
pp. 2050013
Author(s):  
Sawsan Morkos Gharghory
Keyword(s):  
Time Series ◽  
Relative Humidity ◽  
Time Series Data ◽  
Short Term Memory ◽  
Weather Data ◽  
Series Data ◽  
Short Term ◽  
Term Memory ◽  
Long Short Term Memory ◽  
Lstm Network

An enhanced architecture of recurrent neural network based on Long Short-Term Memory (LSTM) is suggested in this paper for predicting the microclimate inside the greenhouse through its time series data. The microclimate inside the greenhouse largely affected by the external weather variations and it has a great impact on the greenhouse crops and its production. Therefore, it is a massive importance to predict the microclimate inside greenhouse as a preceding stage for accurate design of a control system that could fulfill the requirements of suitable environment for the plants and crop managing. The LSTM network is trained and tested by the temperatures and relative humidity data measured inside the greenhouse utilizing the mathematical greenhouse model with the outside weather data over 27 days. To evaluate the prediction accuracy of the suggested LSTM network, different measurements, such as Root Mean Square Error (RMSE) and Mean Absolute Error (MAE), are calculated and compared to those of conventional networks in references. The simulation results of LSTM network for forecasting the temperature and relative humidity inside greenhouse outperform over those of the traditional methods. The prediction results of temperature and humidity inside greenhouse in terms of RMSE approximately are 0.16 and 0.62 and in terms of MAE are 0.11 and 0.4, respectively, for both of them.

scholarly journals Lbl - Lstm : Log Bilinear And Long Short Term Memory Based Efficient Stock Forecasting Model Considering External Fluctuating Factor

2020 ◽  
Vol 9 (4) ◽  
pp. 2057-2063 ◽  
Keyword(s):  
Stock Market ◽  
Short Term Memory ◽  
Machine Learning Algorithms ◽  
Forecasting Model ◽  
Short Term ◽  
Stock Market Prediction ◽  
Term Memory ◽  
Stock Market Performance ◽  
Long Short Term Memory ◽  
Lending Rates

Stock market prediction problem is considered to be NP-hard problem because of highly volatile nature of stock market. In this paper, effort has been made to design efficient stock forecasting model using log Bilinear and long short term memory (LBL-LSTM) considering external fluctuating factor such as varying Bank's lending rates. The external factor bank's lending rates affects stock market performance ,as it plays vital role for the purchase of stocks in case of financial crisis faced by various business enterprises. Proposed LBL-LSTM based model shows performance improvement over existing machine learning algorithms used for stock market prediction.

scholarly journals Lion Algorithm-Optimized Long Short-Term Memory Network for Groundwater Level Forecasting in Udupi District, India

2020 ◽  
Vol 2020 ◽  
pp. 1-8 ◽  
Author(s):  
B. S. Supreetha ◽  
Narayan Shenoy ◽  
Prabhakar Nayak
Keyword(s):  
Groundwater Level ◽  
Water Resource Management ◽  
Short Term Memory ◽  
Forecasting Model ◽  
Short Term ◽  
Term Memory ◽  
Lion Algorithm ◽  
Karnataka State ◽  
Drying Up ◽  
Long Short Term Memory

Groundwater is a precious natural resource. Groundwater level (GWL) forecasting is crucial in the field of water resource management. Measurement of GWL from observation-wells is the principle source of information about the aquifer and is critical to its evaluation. Most part of the Udupi district of Karnataka State in India consists of geological formations: lateritic terrain and gneissic complex. Due to the topographical ruggedness and inconsistency in rainfall, the GWL in Udupi region is declining continually and most of the open wells are drying-up during the summer. Hence, the current research aimed at developing a groundwater level forecasting model by using hybrid long short-term memory-lion algorithm (LSTM-LA). The historical GWL and rainfall data from an observation well from Udupi district, located in Karnataka state, India, were used to develop the model. The prediction accuracy of the hybrid LSTM-LA model was better than that of the feedforward neural network (FFNN) and the isolated LSTM models. The hybrid LSTM-LA-based forecasting model is promising for a larger dataset.

scholarly journals Impact of Uncertainty in the Input Variables and Model Parameters on Predictions of a Long Short Term Memory (LSTM) Based Sales Forecasting Model

2020 ◽  
Vol 2 (3) ◽  
pp. 256-270
Author(s):  
Shakti Goel ◽  
Rahul Bajpai
Keyword(s):  
Short Term Memory ◽  
Forecasting Model ◽  
Model Parameters ◽  
Prediction Errors ◽  
Short Term ◽  
Term Memory ◽  
Corona Virus ◽  
Long Short Term Memory ◽  
Input Variables ◽  
The Impact

A Long Short Term Memory (LSTM) based sales model has been developed to forecast the global sales of hotel business of Travel Boutique Online Holidays (TBO Holidays). The LSTM model is a multivariate model; input to the model includes several independent variables in addition to a dependent variable, viz., sales from the previous step. One of the input variables, “number of active bookers per day”, is estimated for the same day as sales. This need for estimation requires the development of another LSTM model to predict the number of active bookers per day. The number of active bookers is variable, so the predicted is used as an input to the sales forecasting model. The use of a predicted variable as an input variable to another model increases the chance of uncertainty entering the system. This paper discusses the quantum of variability observed in sales predictions for various uncertainties or noise due to the estimation of the number of active bookers. For the purposes of this study, different noise distributions such as normalized, uniform, and logistic distributions are used, among others. Analyses of predictions demonstrate that the addition of uncertainty to the number of active bookers via dropouts as well as to the lagged sales variables leads to model predictions that are close to the observations. The least squared error between observations and predictions is higher for uncertainties modeled using other distributions (without dropouts) with the worst predictions being for Gumbel noise distribution. Gaussian noise added directly to the weights matrix yields the best results (minimum prediction errors). One possibility of this uncertainty could be that the global minimum of the least squared objective function with respect to the model weight matrix is not reached, and therefore, model parameters are not optimal. The two LSTM models used in series are also used to study the impact of corona virus on global sales. By introducing a new variable called the corona virus impact variable, the LSTM models can predict corona-affected sales within five percent (5%) of the actuals. The research discussed in the paper finds LSTM models to be effective tools that can be used in the travel industry as they are able to successfully model the trends in sales. These tools can be reliably used to simulate various hypothetical scenarios also.

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