Sökning: "augmentation of multivariate time series"

Hittade 5 uppsatser innehållade orden augmentation of multivariate time series.

1. 1. Restaurant Daily Revenue Prediction : Utilizing Synthetic Time Series Data for Improved Model Performance

   Uppsats för yrkesexamina på avancerad nivå, Uppsala universitet/Avdelningen för beräkningsvetenskap

   Författare :Stella Jarlöv; Anton Svensson Dahl; [2023]
   Nyckelord :demand forecasting; data augmentation; time series data; machine learning; restaurant industry; generative adversarial networks; TimeGAN; XGBoost;

   Sammanfattning : This study aims to enhance the accuracy of a demand forecasting model, XGBoost, by incorporating synthetic multivariate restaurant time series data during the training process. The research addresses the limited availability of training data by generating synthetic data using TimeGAN, a generative adversarial deep neural network tailored for time series data. LÄS MER

3. 2. AUGMENTATION AND CLASSIFICATION OF TIME SERIES FOR FINDING ACL INJURIES

   Kandidat-uppsats, Umeå universitet/Institutionen för datavetenskap

   Författare :Marie-Louise Johansson; [2022]
   Nyckelord :computer science; machine learning; motion analysis; reconstructed ACL; anterior cruciate ligament; time series forest; dynamic time wapring; ACL; multivariate time series clasification; MTSC; time series classification; TSC; euclidean barycentric average; euclidean barycentric averaging; autmentation of time series; augmentation of multivariate time series; data augmentation; augmentation;

   Sammanfattning : This thesis addresses the problem where we want to apply machine learning over a small data set of multivariate time series. A challenge when classifying data is when the data set is small and overfitting is at risk. Augmentation of small data sets might avoid overfitting. LÄS MER

4. 3. Multivariate Time Series Data Generation using Generative Adversarial Networks : Generating Realistic Sensor Time Series Data of Vehicles with an Abnormal Behaviour using TimeGAN

   Master-uppsats, KTH/Skolan för elektroteknik och datavetenskap (EECS)

   Författare :Sofia Nord; [2021]
   Nyckelord :Time Series Data Generation; Generative Adversarial Network; Deep Neural Network; Data Augmentation; Synthetic Data Generation; Generering av Tidsseriedata; Generativa Motstridande Nätverk; Djupa Neurala Nätverk; Dataökning; Syntetisk Datagenerering;

   Sammanfattning : Large datasets are a crucial requirement to achieve high performance, accuracy, and generalisation for any machine learning task, such as prediction or anomaly detection, However, it is not uncommon for datasets to be small or imbalanced since gathering data can be difficult, time-consuming, and expensive. In the task of collecting vehicle sensor time series data, in particular when the vehicle has an abnormal behaviour, these struggles are present and may hinder the automotive industry in its development. LÄS MER

5. 4. Synthesis of sequential data

   Uppsats för yrkesexamina på avancerad nivå, Uppsala universitet/Avdelningen för systemteknik

   Författare :Joel Viklund; [2021]
   Nyckelord :Synthetic data; TCN; Temporal convolutional networks; TimeGAN;

   Sammanfattning : Good generative models for short time series data exist and have been applied for both data augmentation and privacy protection purposes in the past. A common theme for existing generative models is that they all use a recurrent neural network (RNN) architecture, which makes the models limited regarding the length of the sequences. LÄS MER

7. 5. Emotion Detection from Electroencephalography Data with Machine Learning : Classification of emotions elicited by auditory stimuli from music on self-collected data sets

   Master-uppsats, KTH/Skolan för kemi, bioteknologi och hälsa (CBH)

   Författare :Filip Söderqvist; [2021]
   Nyckelord :Electroencephalography; Deep learning; Machine learning; Likeability; Valence; Arousal; Emotion detection; Elektroencefalografi; Djupinlärning; Maskininlärning; Omtyckthet; Valens; Aktiveringsgrad; Känslodetektion;

   Sammanfattning : The recent advances in deep learning have made it state-of-the-art for many different tasks, making its potential usefulness for analyzing electroencephalography (EEG) data appealing. This study aims at automatic feature extraction and classification of likeability, valence, and arousal elicited by auditory stimuli from music by training deep neural networks (DNNs) on  minimally pre-processed multivariate EEG time series. LÄS MER