DeepAI AI Chat
Log In Sign Up

Parameter Efficient Deep Probabilistic Forecasting

Probabilistic time series forecasting is crucial in many application domains such as retail, ecommerce, finance, or biology. With the increasing availability of large volumes of data, a number of neural architectures have been proposed for this problem. In particular, Transformer-based methods achieve state-of-the-art performance on real-world benchmarks. However, these methods require a large number of parameters to be learned, which imposes high memory requirements on the computational resources for training such models. To address this problem, we introduce a novel Bidirectional Temporal Convolutional Network (BiTCN), which requires an order of magnitude less parameters than a common Transformer-based approach. Our model combines two Temporal Convolutional Networks (TCNs): the first network encodes future covariates of the time series, whereas the second network encodes past observations and covariates. We jointly estimate the parameters of an output distribution via these two networks. Experiments on four real-world datasets show that our method performs on par with four state-of-the-art probabilistic forecasting methods, including a Transformer-based approach and WaveNet, on two point metrics (sMAPE, NRMSE) as well as on a set of range metrics (quantile loss percentiles) in the majority of cases. Secondly, we demonstrate that our method requires significantly less parameters than Transformer-based methods, which means the model can be trained faster with significantly lower memory requirements, which as a consequence reduces the infrastructure cost for deploying these models.


page 1

page 2

page 3

page 4


Probabilistic Time Series Forecasting with Implicit Quantile Networks

Here, we propose a general method for probabilistic time series forecast...

FPTN: Fast Pure Transformer Network for Traffic Flow Forecasting

Traffic flow forecasting is challenging due to the intricate spatio-temp...

TCCT: Tightly-Coupled Convolutional Transformer on Time Series Forecasting

Time series forecasting is essential for a wide range of real-world appl...

Enhancing the Locality and Breaking the Memory Bottleneck of Transformer on Time Series Forecasting

Time series forecasting is an important problem across many domains, inc...

DANLIP: Deep Autoregressive Networks for Locally Interpretable Probabilistic Forecasting

Despite the high performance of neural network-based time series forecas...

Probabilistic Forecasting with Temporal Convolutional Neural Network

We present a probabilistic forecasting framework based on convolutional ...

Code Repositories


Parameter Efficient Deep Probabilistic Forecasting

view repo