Long Short-Term Memory forecasting model for dengue fever cases in Malang regency, Indonesia

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.

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

Applied Computational Intelligence and Soft Computing ◽

10.1155/2020/8685724 ◽

2020 ◽

Vol 2020 ◽

pp. 1-8 ◽

Cited By ~ 1

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.

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

Machine Learning and Knowledge Extraction ◽

10.3390/make2030014 ◽

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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Dengue Fever Detection using Long Short-term Memory Neural Network

2020 17th International Conference on Electrical Engineering/Electronics, Computer, Telecommunications and Information Technology (ECTI-CON) ◽

10.1109/ecti-con49241.2020.9158315 ◽

2020 ◽

Author(s):

Wanchaloem Nadda ◽

Waraporn Boonchieng ◽

Ekkarat Boonchieng

Keyword(s):

Neural Network ◽

Dengue Fever ◽

Short Term Memory ◽

Short Term ◽

Term Memory ◽

Long Short Term Memory

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Electricity Load and Price Forecasting Using Jaya-Long Short Term Memory (JLSTM) in Smart Grids

Entropy ◽

10.3390/e22010010 ◽

2019 ◽

Vol 22 (1) ◽

pp. 10 ◽

Cited By ~ 11

Author(s):

Rabiya Khalid ◽

Nadeem Javaid ◽

Fahad A. Al-zahrani ◽

Khursheed Aurangzeb ◽

Emad-ul-Haq Qazi ◽

...

Keyword(s):

Big Data ◽

Short Term Memory ◽

Demand Forecasting ◽

Forecasting Model ◽

Short Term ◽

Forecasting Accuracy ◽

Term Memory ◽

Proposed Model ◽

Multiple Variables ◽

Long Short Term Memory

In the smart grid (SG) environment, consumers are enabled to alter electricity consumption patterns in response to electricity prices and incentives. This results in prices that may differ from the initial price pattern. Electricity price and demand forecasting play a vital role in the reliability and sustainability of SG. Forecasting using big data has become a new hot research topic as a massive amount of data is being generated and stored in the SG environment. Electricity users, having advanced knowledge of prices and demand of electricity, can manage their load efficiently. In this paper, a recurrent neural network (RNN), long short term memory (LSTM), is used for electricity price and demand forecasting using big data. Researchers are working actively to propose new models of forecasting. These models contain a single input variable as well as multiple variables. From the literature, we observed that the use of multiple variables enhances the forecasting accuracy. Hence, our proposed model uses multiple variables as input and forecasts the future values of electricity demand and price. The hyperparameters of this algorithm are tuned using the Jaya optimization algorithm to improve the forecasting ability and increase the training mechanism of the model. Parameter tuning is necessary because the performance of a forecasting model depends on the values of these parameters. Selection of inappropriate values can result in inaccurate forecasting. So, integration of an optimization method improves the forecasting accuracy with minimum user efforts. For efficient forecasting, data is preprocessed and cleaned from missing values and outliers, using the z-score method. Furthermore, data is normalized before forecasting. The forecasting accuracy of the proposed model is evaluated using the root mean square error (RMSE) and mean absolute error (MAE). For a fair comparison, the proposed forecasting model is compared with univariate LSTM and support vector machine (SVM). The values of the performance metrics depict that the proposed model has higher accuracy than SVM and univariate LSTM.

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