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.

scholarly journals Long Short-Term Memory Networks for Traffic Flow Forecasting: Exploring Input Variables, Time Frames and Multi-Step Approaches

Informatica ◽  
2020 ◽  
pp. 1-27
Author(s):  
Bruno Fernandes ◽  
Fabio Silva ◽  
Hector Alaiz-Moreton ◽  
Paulo Novais ◽  
Jose Neves ◽  
...  
Keyword(s):  
Traffic Flow ◽  
Short Term Memory ◽  
Short Term ◽  
Term Memory ◽  
Traffic Flow Forecasting ◽  
Long Short Term Memory ◽  
Input Variables ◽  
Time Frames

Production Forecasting with the Interwell Interference by Integrating Graph Convolutional and Long Short-Term Memory Neural Network

2021 ◽  
pp. 1-17
Author(s):  
Enda Du ◽  
Yuetian Liu ◽  
Ziyan Cheng ◽  
Liang Xue ◽  
Jing Ma ◽  
...  
Keyword(s):  
Neural Network ◽  
Short Term Memory ◽  
Short Term ◽  
Term Memory ◽  
Production Forecasting ◽  
Temporal Correlations ◽  
Proposed Model ◽  
The Mean ◽  
Long Short Term Memory ◽  
The Impact

Summary Accurate production forecasting is an essential task and accompanies the entire process of reservoir development. With the limitation of prediction principles and processes, the traditional approaches are difficult to make rapid predictions. With the development of artificial intelligence, the data-driven model provides an alternative approach for production forecasting. To fully take the impact of interwell interference on production into account, this paper proposes a deep learning-based hybrid model (GCN-LSTM), where graph convolutional network (GCN) is used to capture complicated spatial patterns between each well, and long short-term memory (LSTM) neural network is adopted to extract intricate temporal correlations from historical production data. To implement the proposed model more efficiently, two data preprocessing procedures are performed: Outliers in the data set are removed by using a box plot visualization, and measurement noise is reduced by a wavelet transform. The robustness and applicability of the proposed model are evaluated in two scenarios of different data types with the root mean square error (RMSE), the mean absolute error (MAE), and the mean absolute percentage error (MAPE). The results show that the proposed model can effectively capture spatial and temporal correlations to make a rapid and accurate oil production forecast.

scholarly journals Analysis of Groundwater Level Variations Caused by the Changes in Groundwater Withdrawals Using Long Short-Term Memory Network

Hydrology ◽  
2020 ◽  
Vol 7 (3) ◽  
pp. 64 ◽  
Author(s):  
Mun-Ju Shin ◽  
Soo-Hyoung Moon ◽  
Kyung Goo Kang ◽  
Duk-Chul Moon ◽  
Hyuk-Joon Koh
Keyword(s):  
Groundwater Level ◽  
Short Term Memory ◽  
Groundwater Resources ◽  
Prediction Performance ◽  
Groundwater Withdrawal ◽  
Short Term ◽  
Maximum Increase ◽  
Term Memory ◽  
Long Short Term Memory ◽  
The Impact

To properly manage the groundwater resources, it is necessary to analyze the impact of groundwater withdrawal on the groundwater level. In this study, a Long Short-Term Memory (LSTM) network was used to evaluate the groundwater level prediction performance and analyze the impact of the change in the amount of groundwater withdrawal from the pumping wells on the change in the groundwater level in the nearby monitoring wells located in Jeju Island, Korea. The Nash–Sutcliffe efficiency between the observed and simulated groundwater level was over 0.97. Therefore, the groundwater prediction performance of LSTM was remarkably high. If the groundwater level is simulated on the assumption that the future withdrawal amount is reduced by 1/3 of the current groundwater withdrawal, the range of the maximum rise of the groundwater level would be 0.06–0.13 m compared to the current condition. In addition, assuming that no groundwater is taken, the range of the maximum increase in the groundwater level would be 0.11–0.38 m more than the current condition. Therefore, the effect of groundwater withdrawal on the groundwater level in this area was exceedingly small. The method and results can be used to develop new groundwater withdrawal sources for the redistribution of groundwater withdrawals.

scholarly journals Impact of COVID-19 on Forecasting Stock Prices: An Integration of Stationary Wavelet Transform and Bidirectional Long Short-Term Memory

Complexity ◽  
2020 ◽  
Vol 2020 ◽  
pp. 1-12 ◽  
Author(s):  
Daniel Štifanić ◽  
Jelena Musulin ◽  
Adrijana Miočević ◽  
Sandi Baressi Šegota ◽  
Roman Šubić ◽  
...  
Keyword(s):  
Crude Oil ◽  
Stock Prices ◽  
Short Term Memory ◽  
Oil Price ◽  
Stationary Wavelet Transform ◽  
Crude Oil Price ◽  
Short Term ◽  
Term Memory ◽  
Long Short Term Memory ◽  
The Impact

COVID-19 is an infectious disease that mostly affects the respiratory system. At the time of this research being performed, there were more than 1.4 million cases of COVID-19, and one of the biggest anxieties is not just our health, but our livelihoods, too. In this research, authors investigate the impact of COVID-19 on the global economy, more specifically, the impact of COVID-19 on the financial movement of Crude Oil price and three US stock indexes: DJI, S&P 500, and NASDAQ Composite. The proposed system for predicting commodity and stock prices integrates the stationary wavelet transform (SWT) and bidirectional long short-term memory (BDLSTM) networks. Firstly, SWT is used to decompose the data into approximation and detail coefficients. After decomposition, data of Crude Oil price and stock market indexes along with COVID-19 confirmed cases were used as input variables for future price movement forecasting. As a result, the proposed system BDLSTM + WT-ADA achieved satisfactory results in terms of five-day Crude Oil price forecast.

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

Deep learning reservoir porosity prediction based on multilayer long short-term memory network

Geophysics ◽  
2020 ◽  
Vol 85 (4) ◽  
pp. WA213-WA225
Author(s):  
Wei Chen ◽  
Liuqing Yang ◽  
Bei Zha ◽  
Mi Zhang ◽  
Yangkang Chen
Keyword(s):  
Oil And Gas ◽  
High Efficiency ◽  
Short Term Memory ◽  
Southern China ◽  
Prediction Errors ◽  
Short Term ◽  
Drilling Depth ◽  
Term Memory ◽  
Prediction Task ◽  
Long Short Term Memory

The cost of obtaining a complete porosity value using traditional coring methods is relatively high, and as the drilling depth increases, the difficulty of obtaining the porosity value also increases. Nowadays, the prediction of fine reservoir parameters for oil and gas exploration is becoming more and more important. Therefore, high-efficiency and low-cost prediction of porosity based on logging data is necessary. We have developed a machine-learning method based on the traditional long short-term memory (LSTM) model, called multilayer LSTM (MLSTM), to perform the porosity prediction task. We used three different wells in a block in southern China for the prediction task, including a training well and two test wells. One test well has the same logging data type as the training well, whereas the other test well differs from the training well in the logging depth and parameter types. Two different types of test data sets are used to detect the generalization ability of the network. A set of data was used to train the MLSTM network, and the hyperparameters of the network were adjusted through experimental accuracy feedback. We also tested the performance of the network using two sets of log data from different regions, including generalization and sensitivity of the network. During the training phase of the porosity prediction model, the developed MLSTM establishes a minimized objective function, uses the Adam optimization algorithm to update the weight of the network, and adjusts the network hyperparameters to select the best target according to the feedback of the network accuracy. Compared with conventional sequence neural networks, such as the gated recurrent unit and recurrent neural network, the logging data experiments show that MLSTM has better robustness and accuracy in depth sequence prediction. Especially, the porosity value at the depth inflection point can be better predicted when the trend of the depth sequence was predicted. This framework is expected to reduce the porosity prediction errors when data are insufficient and log depths are different.

scholarly journals Using long short-term memory networks for river flow prediction

2020 ◽  
Vol 51 (6) ◽  
pp. 1358-1376
Author(s):  
Wei Xu ◽  
Yanan Jiang ◽  
Xiaoli Zhang ◽  
Yi Li ◽  
Run Zhang ◽  
...  
Keyword(s):  
River Flow ◽  
Short Term Memory ◽  
River Basins ◽  
Short Term ◽  
Term Memory ◽  
Learning Efficiency ◽  
Flow Prediction ◽  
Non Linear ◽  
Long Short Term Memory ◽  
The Impact

Abstract Deep learning has made significant advances in methodologies and practical applications in recent years. However, there is a lack of understanding on how the long short-term memory (LSTM) networks perform in river flow prediction. This paper assesses the performance of LSTM networks to understand the impact of network structures and parameters on river flow predictions. Two river basins with different characteristics, i.e., Hun river and Upper Yangtze river basins, are used as case studies for the 10-day average flow predictions and the daily flow predictions, respectively. The use of the fully connected layer with the activation function before the LSTM cell layer can substantially reduce learning efficiency. On the contrary, non-linear transformation following the LSTM cells is required to improve learning efficiency due to the different magnitudes of precipitation and flow. The batch size and the number of LSTM cells are sensitive parameters and should be carefully tuned to achieve a balance between learning efficiency and stability. Compared with several hydrological models, the LSTM network achieves good performance in terms of three evaluation criteria, i.e., coefficient of determination, Nash–Sutcliffe Efficiency and relative error, which demonstrates its powerful capacity in learning non-linear and complex processes in hydrological modelling.

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