Systems and methods for time series prediction using multi-stage computation

A method for generating time series predictions from a first dataset comprising a first plurality of data entries and having a first feature space, the method executed in a computing environment comprising one or more processors and memory, the method comprising:

• encoding the first dataset to generate a latent vector having a latent space smaller than the first feature space;
• for a first time period (n=1) in a plurality of time periods:
• processing the latent vector using an attention mechanism to generate a first attention vector;
• processing the first attention vector using an LSTM (Long Short-Term Memory) neural network model to generate a first latent prediction vector;
• decoding the first latent prediction vector to generate a first prediction vector of a plurality of time series prediction vectors having a second feature space larger than the latent space;
• for each successive nth time period (n>1) in the plurality of time periods:
• processing the latent vector and an (n−1)th latent prediction vector using the attention mechanism to generate an nth attention vector;
• processing the nth attention vector using the LSTM (Long Short-Term Memory) neural network model to generate an nth latent prediction vector;
• decoding the nth latent prediction vector to generate an nth prediction vector of the plurality of time series prediction vectors in the second feature space;
• classifying the first dataset using an XGBoost (Extreme Gradient Boosting) classifier to generate a classified dataset, the classified dataset comprising a set of probability weights from 0 to 1;
• scaling each of the plurality of time series prediction vectors based on the classified dataset to generate a weighted plurality of time series prediction vectors, each of the weighted plurality of time series prediction vectors having a plurality of prediction values corresponding to the first plurality of data entries.

A non-transitory computer readable medium storing computer executable instructions which, when executed by at least one computer processor, cause the at least one computer processor to carry out a method of generating time series predictions from a first dataset comprising a first plurality of data entries and having a first feature space, the method comprising:

• encoding the first dataset to generate a latent vector having a latent space smaller than the first feature space;
• for a first time period (n=1) in a plurality of time periods:
• processing the latent vector using an attention mechanism to generate a first attention vector;
• processing the first attention vector using an LSTM (Long Short-Term Memory) neural network model to generate a first latent prediction vector;
• decoding the first latent prediction vector to generate a first prediction vector of a plurality of time series prediction vectors having a second feature space larger than the latent space;
• for each successive nth time period (n>1) in the plurality of time periods:
• processing the latent vector and an (n−1)th latent prediction vector using the attention mechanism to generate an nth attention vector;
• processing the nth attention vector using the LSTM (Long Short-Term Memory) neural network model to generate an nth latent prediction vector;
• decoding the nth latent prediction vector to generate an nth prediction vector of the plurality of time series prediction vectors in the second feature space;
• classifying the first dataset using an XGBoost (Extreme Gradient Boosting) classifier to generate a classified dataset, the classified dataset comprising a set of probability weights from 0 to 1;
• scaling each of the plurality of time series prediction vectors based on the classified dataset to generate a weighted plurality of time series prediction vectors, each of the weighted plurality of time series prediction vectors having a plurality of prediction values corresponding to the first plurality of data entries.