Essays about: "Long Short-Term Memory LSTMs"

Showing result 1 - 5 of 20 essays containing the words Long Short-Term Memory LSTMs.

1. 1. Forecasting With Feature-Based Time Series Clustering

   University essay from Jönköping University/Tekniska Högskolan

   Author : Conrad Tingström; Johan Åkerblom Svensson; [2023]
   Keywords : demand forecasting; time series prediction; time series clustering;

   Abstract : Time series prediction plays a pivotal role in various areas, including for example finance, weather forecasting, and traffic analysis. In this study, time series of historical sales data from a packaging manufacturer is used to investigate the effects that clustering such data has on forecasting performance. READ MORE

3. 2. Temporal Localization of Representations in Recurrent Neural Networks

   University essay from Högskolan Dalarna/Institutionen för information och teknik

   Author : Asadullah Najam; [2023]
   Keywords : Recurrent Neural Networks RNNs ; Deep Learning; Time Series Prediction; Exploding Values; Gradient Decay; Long Short-Term Memory LSTMs ; Gated Recurrent Units GRUs ; Attention Mechanism; Moving Representations; Localizing Representations;

   Abstract : Recurrent Neural Networks (RNNs) are pivotal in deep learning for time series prediction, but they suffer from 'exploding values' and 'gradient decay,' particularly when learning temporally distant interactions. Long Short-Term Memory (LSTM) and Gated Recurrent Units (GRU) have addressed these issues to an extent, but the precise mitigating mechanisms remain unclear. READ MORE

4. 3. Insurance Fraud Detection using Unsupervised Sequential Anomaly Detection

   University essay from Linköpings universitet/Institutionen för datavetenskap

   Author : Anton Hansson; Hugo Cedervall; [2022]
   Keywords : Insurance Fraud Detection; Anomaly Detection; Long Short-Term Memory Networks LSTM ; Unsupervised Learning; Autoencoder AE ; Variational Autoencoder VAE ; Interpretable Machine Learning; Feature Engineering; Feature Selection; Feature Importance;

   Abstract : Fraud is a common crime within the insurance industry, and insurance companies want to quickly identify fraudulent claimants as they often result in higher premiums for honest customers. Due to the digital transformation where the sheer volume and complexity of available data has grown, manual fraud detection is no longer suitable. READ MORE

5. 4. Investigating the Attribution Quality of LSTM with Attention and SHAP : Going Beyond Predictive Performance

   University essay from KTH/Skolan för elektroteknik och datavetenskap (EECS)

   Author : Hannes Kindbom; [2021]
   Keywords : Digital marketing; Attribution modelling; Multi-touch attribution; Deep learning; LSTM; SHAP; Attention; Interpretability; Digital marknadsföring; Attributionsmodellering; Multi-touch attribution; Djupinlärning; LSTM; SHAP; Attention; Tolkningsbarhet;

   Abstract : Estimating each marketing channel’s impact on conversion can help advertisers develop strategies and spend their marketing budgets optimally. This problem is often referred to as attribution modelling, and it is gaining increasing attention in both the industry and academia as access to online tracking data improves. READ MORE

6. 5. Using LSTM Neural Networks To Predict Daily Stock Returns

   University essay from Linnéuniversitetet/Institutionen för datavetenskap och medieteknik (DM)

   Author : Jon William Cavallie Mester; [2021]
   Keywords : LSTM; RNN; stock prediction; deep learning;

   Abstract : Long short-term memory (LSTM) neural networks have been proven to be effective for time series prediction, even in some instances where the data is non-stationary. This lead us to examine their predictive ability of stock market returns, as the development of stock prices and returns tend to be a non-stationary time series. READ MORE