Zobrazeno 1 - 10
of 45
pro vyhledávání: '"Achar, Avinash"'
Arrival/Travel times for public transit exhibit variability on account of factors like seasonality, dwell times at bus stops, traffic signals, travel demand fluctuation etc. The developing world in particular is plagued by additional factors like lac
Externí odkaz:
http://arxiv.org/abs/2210.01655
Autor:
Pachal, Soumen, Achar, Avinash
Missing data scenarios are very common in ML applications in general and time-series/sequence applications are no exceptions. This paper pertains to a novel Recurrent Neural Network (RNN) based solution for sequence prediction under missing data. Our
Externí odkaz:
http://arxiv.org/abs/2208.08933
Autor:
Achar, Avinash, Pachal, Soumen
Deep learning (DL) in general and Recurrent neural networks (RNNs) in particular have seen high success levels in sequence based applications. This paper pertains to RNNs for time series modelling and forecasting. We propose a novel RNN architecture
Externí odkaz:
http://arxiv.org/abs/2207.04113
Providing real time information about the arrival time of the transit buses has become inevitable in urban areas to make the system more user-friendly and advantageous over various other transportation modes. However, accurate prediction of arrival t
Externí odkaz:
http://arxiv.org/abs/1904.03444
Publikováno v:
In Transportation Research Part C November 2022 144
Predicting travel times of vehicles in urban settings is a useful and tangible quantity of interest in the context of intelligent transportation systems. We address the problem of travel time prediction in arterial roads using data sampled from probe
Externí odkaz:
http://arxiv.org/abs/1711.05767
Autor:
Achar, Avinash
Pattern Discovery, a popular paradigm in data mining refers to a class of techniques that try and extract some unknown or interesting patterns from data. The work carried out in this thesis concerns frequent episode mining, a popular framework within
Externí odkaz:
http://etd.iisc.ernet.in/handle/2005/2024
http://etd.ncsi.iisc.ernet.in/abstracts/2619/G24923-Abs.pdf
http://etd.ncsi.iisc.ernet.in/abstracts/2619/G24923-Abs.pdf
Frequent Episode Discovery framework is a popular framework in Temporal Data Mining with many applications. Over the years many different notions of frequencies of episodes have been proposed along with different algorithms for episode discovery. In
Externí odkaz:
http://arxiv.org/abs/1007.0690
Frequent episode discovery is a popular framework for pattern discovery in event streams. An episode is a partially ordered set of nodes with each node associated with an event type. Efficient (and separate) algorithms exist for episode discovery whe
Externí odkaz:
http://arxiv.org/abs/0902.1227