Abstract: Embodiments described herein provide a Transformer architecture for time series data forecasting. Specifically, the Transformer based time series model may be built on a transformer architecture having one or more multi patch size projection layers in the encoder and the decoder, and an any-variate attention module. The Transformer based time series model may receive multivariate time series and consider all variates as a single sequence. Patches of the input are subsequently projected into vector representations via a multi patch size input projection layer. The output tokens of forecasted time series data are then decoded via the multi patch size output projection layers in the parameters of the mixture distribution.

Abstract: Embodiments described herein provide a time-index model for forecasting time-series data. The architecture of the model takes a normalized time index as an input, uses a model, g_?, to produce a vector representation of the time-index, and uses a “ridge regressor” which takes the vector representation and provides an estimated value. The model may be trained on a time-series dataset. The ridge regressor is trained for a given g_? to reproduce a given lookback window. g_? is trained over time-indexes in a horizon window, such that g_? and the corresponding ridge regressor will accurately predict the data in the horizon window. Once g_? is sufficiently trained, the ridge regressor can be updated based on that final g_? over a lookback window comprising the time-indexes with the last known values. The final g_? together with the updated ridge regressor can be given time-indexes past the known values, thereby providing forecasted values.

Abstract: Embodiments described herein provide a method of forecasting time series data at future timestamps in a dynamic system. The method of forecasting time series data also includes receiving, via a data interface, a time series dataset. The method also includes determining, via a frequency attention layer, a seasonal representation based on a frequency domain analysis of the time series data. The method also includes determining, via an exponential attention layer, a growth representation based on the seasonal representation. The method also includes generating, via a decoder, a time series forecast based on the seasonal representation and the trend representation.

Abstract: A method includes receiving, via a data interface, a training dataset of time-series data samples; and generating, by an encoder of a representation training model, intermediate representations of a training data sample from the training dataset. One or more trend feature representations are generated based on the intermediate representations. One or more seasonal feature representations are generated based on the intermediate representations. The representation training model is trained, using the one or more trend feature representations and one or more seasonal feature representations, to generate a trained representation training model.

Abstract: A multi-view contrastive relational learning framework is provided. In the multi-view contrastive relational learning framework, contrastive learning is augmented with a multi-view learning signal. The auxiliary views guide an encoder of the underlying time series data's main view, by using an inter-sample similarity structure as a learning signal to learn representations which encode information from multiple views.