Sökning: "Contextual Time Series"

Visar resultat 1 - 5 av 14 uppsatser innehållade orden Contextual Time Series.

1. 1. SAX meets Word2vec : A new paradigm in the time series forecasting

   Uppsats för yrkesexamina på avancerad nivå, Högskolan i Halmstad/Akademin för informationsteknologi

   Författare :Erik Janerdal; David Dimovski; [2023]
   Nyckelord :Contextual Time Series; Forecasting; ARIMA; SARIMA; Word2vec; SAX;

   Sammanfattning : The purpose of this thesis was to investigate whether some successful ideas in NLP, such as word2vec, are applicable to time series prob- lems or not. More specifically, we are interested to assess a combina- tion of previously proven methods such as SAX and Word2vec. LÄS MER

3. 2. Enhancing failure prediction from timeseries histogram data : through fine-tuned lower-dimensional representations

   Master-uppsats, Högskolan i Halmstad/Akademin för informationsteknologi

   Författare :Vijay Jayaraman; [2023]
   Nyckelord :time-series prediction; time-series histogram analysis; Convolution Neural Network CNN ; Autoencoder; Weibull Time-to-Event WTTE ; Recurrent Neural Network RNN ; Engine turbo charger failure prediciton;

   Sammanfattning : Histogram data are widely used for compressing high-frequency time-series signals due to their ability to capture distributional informa-tion. However, this compression comes at the cost of increased di-mensionality and loss of contextual details from the original features. LÄS MER

4. 3. Hybrid Deep Learning Model for Cellular Network Traffic Prediction : Case Study using Telecom Time Series Data, Satellite Imagery, and Weather Data

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

   Författare :Ali Shibli; [2022]
   Nyckelord :Cellular network traffic; multi-modal; satellite imagery; weather data; LSTM; CNN; time series; Trafic sur les réseaux cellulaires; multimodal; imagerie satellite; données météo; LSTM; CNN; séries temporelles; Förutsägelse av mobilnätstrafik; multimodal modell; satellitbilder; väderdata; LSTM; CNN; tidsseriein;

   Sammanfattning : Cellular network traffic prediction is a critical challenge for communication providers, which is important for use cases such as traffic steering and base station resources management. Traditional prediction methods mostly rely on historical time-series data to predict traffic load, which often fail to model the real world and capture surrounding environment conditions. LÄS MER

5. 4. LSTM Feature Engineering Through Time Series Similarity Embedding

   Master-uppsats, Linköpings universitet/Institutionen för datavetenskap

   Författare :Sebastian Bångerius; [2022]
   Nyckelord :Embedding; time series; LSTM; feature engineering; DTW; correlation; prediction;

   Sammanfattning : Time series prediction has many applications. In cases with simultaneous series (like measurements of weather from multiple stations, or multiple stocks on the stock market)it is not unlikely that these series from different measurement origins behave similarly, or respond to the same contextual signals. LÄS MER

7. 5. Unsuperised Anomaly Detection : Methods and Application on Solvency 2 Technical Provisions

   Uppsats för yrkesexamina på avancerad nivå, Umeå universitet/Institutionen för matematik och matematisk statistik

   Författare :Richard Olofsson; [2020]
   Nyckelord :;

   Sammanfattning : This thesis work examines anomaly detection methods on large data sets related to insurance funds. Starting from requirements of low time complexity, ease of implementation and thorough definitions of contextual- and collective anomalies, different modelling frameworks are examined. LÄS MER