A processor implemented method, comprising: receiving, at an input layer, a multi-dimensional time series corresponding to a plurality of parameters specific to a machine, the parameters obtained from one or more sensors connected to the machine; obtaining, using a dimensionality reduction model, a reduced-dimensional time series from the multi-dimensional time series via an at least one feedforward layer, wherein connections between the input layer and the feedforward layer are sparse to access at least a portion of the plurality of parameters, and wherein each of the plurality of parameters in the reduced-dimensional time series is a non-linear function of a subset of the plurality of parameters of the multi-dimensional time series; estimating, by using a recurrent neural network, RNN, encoder-decoder model, the multi-dimensional time series using the reduced-dimensional time series obtained by the dimensionality reduction model; simultaneously learning, by using the estimated multi-dimensional time series, the dimensionality reduction model and the RNN encoder-decoder model to obtain a multi-layered sparse neural network, wherein learning comprises training the sparse feedforward layer of the dimensionality reduction model and the RNN encoder-decoder model using a stochastic gradient descent, and wherein a value of gradient of the sparse feedforward layer of the dimensionality reduction model is based on an element of a sparse weight matrix; computing, by using the multi-layered sparse neural network, a plurality of error vectors corresponding to at least one time instance of the multi-dimensional time series by performing a comparison of the multi-dimensional time series and the estimated multi-dimensional time series; generating at least one anomaly score based on the plurality of the error vectors; and classifying the at least one time instance in the multi-dimensional time series as anomalous based on the at least one anomaly score and a threshold, wherein an anomaly corresponds to a faulty behaviour of the machine. receiving, at an input layer, a multi-dimensional time series corresponding to a plurality of parameters specific to a machine, the parameters obtained from one or more sensors connected to the machine; obtaining, using a dimensionality reduction model, a reduced-dimensional time series from the multi-dimensional time series via an at least one feedforward layer, wherein connections between the input layer and the feedforward layer are sparse to access at least a portion of the plurality of parameters, and wherein each of the plurality of parameters in the reduced-dimensional time series is a non-linear function of a subset of the plurality of parameters of the multi-dimensional time series; estimating, by using a recurrent neural network, RNN, encoder-decoder model, the multi-dimensional time series using the reduced-dimensional time series obtained by the dimensionality reduction model; simultaneously learning, by using the estimated multi-dimensional time series, the dimensionality reduction model and the RNN encoder-decoder model to obtain a multi-layered sparse neural network, wherein learning comprises training the sparse feedforward layer of the dimensionality reduction model and the RNN encoder-decoder model using a stochastic gradient descent, and wherein a value of gradient of the sparse feedforward layer of the dimensionality reduction model is based on an element of a sparse weight matrix; computing, by using the multi-layered sparse neural network, a plurality of error vectors corresponding to at least one time instance of the multi-dimensional time series by performing a comparison of the multi-dimensional time series and the estimated multi-dimensional time series; generating at least one anomaly score based on the plurality of the error vectors; and classifying the at least one time instance in the multi-dimensional time series as anomalous based on the at least one anomaly score and a threshold, wherein an anomaly corresponds to a faulty behaviour of the machine.