Essays about: "unevenly spaced time series"

Found 3 essays containing the words unevenly spaced time series.

  1. 1. Clustering of Unevenly Spaced Mixed Data Time Series

    University essay from KTH/Matematisk statistik

    Author : Pierre Sinander; Asik Ahmed; [2023]
    Keywords : mixed data time series; unevenly spaced time series; clustering; dynamic time warping; Gower dissimilarity; time warping regularisation; numeriska och kategoriska tidsserier; ojämnt fördelade tidsserier; kluster analys; dynamic time warping; Gower dissimilaritet; regularisering av tidsförvränging;

    Abstract : This thesis explores the feasibility of clustering mixed data and unevenly spaced time series for customer segmentation. The proposed method implements the Gower dissimilarity as the local distance function in dynamic time warping to calculate dissimilarities between mixed data time series. READ MORE

  2. 2. Super-Resolution Vehicle Trajectory using Recurrent Time Series Imputation

    University essay from Uppsala universitet/Institutionen för informationsteknologi

    Author : Hasnain Roopawalla; [2022]
    Keywords : ;

    Abstract : Vehicle data finds its use in a variety of applications in the fields of machine learning and data analysis. The volume of available data is limited by the frequency of data collection, and for several reasons, it can be infeasible to simply amplify this frequency. READ MORE

  3. 3. The impact of parsing methods on recurrent neural networks applied to event-based vehicular signal data

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

    Author : Lindblad Max; [2018]
    Keywords : neural network; artificial neural network; ANN; recurrent neural network; RNN; long-short term memory; LSTM; event; slice; parsing method; slice parsing; event parsing; time-slice; event-based; slice-based; signal data; temporal data; temporal sequence; multivariate; time-series; unequally spaced; unevenly spaced; irregularly spaced; Scania; SICS; SAGA;

    Abstract : This thesis examines two different approaches to parsing event-based vehicular signal data to produce input to a neural network prediction model: event parsing, where the data is kept unevenly spaced over the temporal domain, and slice parsing, where the data is made to be evenly spaced over the temporal domain instead. The dataset used as a basis for these experiments consists of a number of vehicular signal logs taken at Scania AB. READ MORE