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.

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.

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.

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.

Abstract: This disclosure relates generally to a method and system for encoder decoder based RNN learning for time series forecasting in presence of missing data. The present disclosure employs atleast one multi-layer RNN encoder and a decoder for time series forecasting. The method receives a plurality of input data comprising a sequential data transformed into a plurality of windows to obtain a plurality of features. Further, the plurality of features is segregated into an available data blocks and a missing data blocks. The first multi-layer RNN encoder fetches the available data blocks and the second multi-layer RNN encoder fetches the missing data blocks to forecast the target variable from the multi-step time series data. The decoder input is generated by appending the combined context vector with an exogeneous variable at each time step.

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.

Abstract: This disclosure relates generally to methods and systems for multiple time-series data forecasting using recurrent neural networks (RNNs). Conventional techniques in the art for the time-series prediction are limited to deal with one long data sequence and a single forecasting model may not be sufficient and efficient to cover the multiple short data sequences. The present disclosure makes use of greedy recursive procedure to build a set of multi-step forecasting models that covers the multiple data sequences, using the recurrent neural network (RNN) models. The one or more multi-step residual error forecasting models makes the forecasting resulting from the set of multi-step forecasting models, accurate and efficient. The set of multi-step forecasting models are useful for various forecasting applications such as prediction of the sales for retail industries, prediction of power consumption for households, the prediction of traffic occupancy across roads, and so on.