... embedding is then used to extract time-encoded cross-sectional features. Encoder. The Portfolio Transformer uses a stack of four identical 64 D. Kisiel and D. Gorse 2.4 Time2Vec Embedding 3 Methodology 3.1 Portfolio Transformer ...

... embedding to insert information about month, hour, and minute into the model. Time2Vec is used in the architectures of [29,31]. The authors of [44] use a revised version of Time2Vec called AddT2Vec. The authors argue, that Time2Vec ...

... Time2Vec. In Time2Vec (Kazemi et al, 2019), time t (or a delta of time as in equation 15.32 and equation 15.34) is ... embedding is of the form h(t,l,i) for v i at time t in layer l using the following steps: 1. For any node vi , h ...

... Time2Vec. Time2Vec is a learnable vector representation of time [13]. Moreover, Kazemi et al. [13] has proved that ... Embedding Fig. 1. Price encoder Sequential Price Encoder day T 5.2 Text Information Encoder The Textual ...

... time - related interaction patterns , we enhance GTEA by inte- grating Enc ( ) with a time - encoder , which is adapted from Time2Vec ( T2V ) [ 13 ] . Specifically , for any given time t , a time embedding 7 ( t ) € R1 + 1 can be ...

... embedding . The key insight of Time2Vec [ 28 ] is recognizing that the positional embedding is just that : a representation of the position of the token with respect to others . Hence , the timestamp is used to calculate the embedding ...

... time-aware multimodal transformer proposed in this work using time-enriched positional embeddings (i.e., time2vec [24]) and sub-sequence sampling. 4.4 Training and Evaluation Details We train all models using Adam [27] optimizer with a ...

... Time2Vec [22] and categorical embeddings [39], that enable the use of categorical features based on the properties of the time-series signature, were added to Seq2Seq. These enhancements improved the performance of Seq2Seq. The proposed ...

... Time2Vec For time series prediction [9, 10], the input sequence values are converted into an embedding vector. This embedding vector is then concatenated with a time vector obtained from a trainable network that uses sine function as ...

... time series into a 1D causal convolutional layer with stride 1 to obtain imputation embedding emimp ∈ Raxdh . Secondly , a time representation , Time2Vec [ 33 ] , is learned to transform each value in a list of continuous time points ...