公开(公告)号：US11809817B2

公开(公告)日：2023-11-07

申请号：US18146863

申请日：2022-12-27

Applicant: Tata Consultancy Services Limited

Inventor： Avinash Achar ,  Soumen Pachal

IPC: G06F17/00 ,  G06F40/177 ,  G06N3/0499 ,  G06N3/063 ,  G06N3/044 ,  G06N3/045

CPC classification number: G06F40/177 ,  G06N3/044 ,  G06N3/045 ,  G06N3/0499 ,  G06N3/063

Abstract: Currently available time-series prediction techniques only factors last observed value from left of missing values and immediate observed value from right is mostly ignored while performing data imputation, thus causing errors in imputation and learning. Present application provides methods and systems for time-series prediction under missing data scenarios. The system first determines missing data values in time-series data. Thereafter, system identifies left data value, right data value, left gap length, right gap length and mean value for each missing data value. Further, system provides left gap length and right gap length identified for each missing data value to feed-forward neural network to obtain importance of left data value, right data value and mean value. The system then passes importance obtained for each missing data value to SoftMax layer to obtain probability distribution that is further utilized to calculate new data value corresponding to each missing data value in time-series data.

公开(公告)号：US11593822B2

公开(公告)日：2023-02-28

申请号：US17481854

申请日：2021-09-22

Applicant: Tata Consultancy Services Limited

Inventor： Avinash Achar ,  Soumen Pachal ,  Antony Joshini Lobo

IPC: G06Q30/02 ,  G06Q30/0202

Abstract: State of the art systems that are used for time series data prediction have the disadvantage that perform only one step prediction, which has only limited application. Disadvantage of such systems is that extent of applications of such single step predictions are limited. The disclosure herein generally relates to time series data prediction, and, more particularly, to time series data prediction based on seasonal lags. The system processes collected input data and determines order of seasonality of the input data. The system further selects encoders based on the determined order of seasonality and generates input data for a decoder that forms encoder-decoder pair with each of the encoders. The system then generates time series data predictions based on seasonal lag information distributed without redundance between encoder and decoder inputs.

公开(公告)号：US12230134B2

公开(公告)日：2025-02-18

申请号：US18066392

申请日：2022-12-15

Applicant: Tata Consultancy Services Limited

Inventor： Soumen Pachal ,  Nancy Bhutani ,  Avinash Achar

IPC: G08G1/123 ,  G01C21/34

Abstract: Arrival/Travel times for public transit exhibit variability on account of factors like seasonality, dwell times at bus stops, traffic signals, travel demand fluctuation, spatial and temporal correlations, etc. The developing world in particular is plagued by additional factors like lack of lane discipline, excess vehicles, diverse modes of transport and so on. This renders the bus arrival time prediction (BATP) to be a challenging problem especially in the developing world. Present disclosure provides system and method that implement recurrent neural networks (RNNs) for BATP (in real-time), wherein the system incorporates information pertaining to spatial and temporal correlations and seasonal correlations. More specifically, a Gated Recurrent Unit (GRU) based Encoder-Decoder (ED) model with one or more bi-directional layers at the decoder is implemented for BATP based on relevant additional synchronized inputs (from previous trips) at each step of the decoder. The system further captures congestion influences on travel time prediction.