# Stride-TCN for Energy Consumption Forecasting and Its Optimization
## 2 Background  
The staleness of the model, quality of data, and long prediction range are usually ill-posed problems in the case of time-series forecasting [5]. Various methods have been proposed to solve the above problems, not only with classical statistics but also with recent deep-learning-based techniques.  
As a representative of traditional methods, autoregressive integrated moving average (ARIMA) [5] has been used for many years in the field of time series modeling. An ARIMA model combines AR and the moving average to account for seasonality, long-term trends, autoregression, and autocorrelation embedded in the data. However, long-term models are inevitably prone to overfitting and high computational costs [3].  
Moreover, deep learning techniques, such as CNNs and RNNs, are also introduced and widely applied for time series forecasting. The advantage that can be gained using a deep learning approach for time series forecasting is that it does not require manually dealing with in-depth data beforehand because representative features are automatically extracted through a training process. The various extensions of RNNs, such as long short-term memory (LSTM) and gated recurrent units (GRU), are suited for time series forecasting [3]. However, these memory and gate-based models still suffer the problem of a long-time dependency on time-series data. In particular, the number of parameters significantly increases even when only a short additional time interval is considered [6].  
The TCN [7] is proposed to consider the long-time dependency issue in time series data. As far as we know, the TCN is currently a prominent model in time series forecasting due to two significant modifications: dilation and causal one-dimensional (1D) convolutional layers. The causal 1D convolutional layer offers a learnable kernel filter with proper padding only on the past side to avoid overprediction. Moreover, dilation is the main factor that grants the TCN the ability to enlarge the history coverage via stacking layers. Therefore, the TCN is very effective in dealing with time-series problems and is easily extensible. However, to ensure the range of history for forecasting, a TCN often stacks many convolutional layers on top of each other, significantly increasing the number of parameters and requiring a considerable time to train the model. With the principles of TCN in mind, we develop a simplified architecture that uses correlation factors from a dataset to achieve a compact model that is robust in terms of model lower complexity and robust performance.  
A large number of existing studies in the broader literature have examined TCN and its variants for various tasks and types of datasets. [PERSON] et al. employed TCN with an interval width adjustment strategy for wind speed forecasting [8]. For the same task of wind speed forecasting, [PERSON] et al. proposed a framework that combined patch transformation, mode decomposition with adaptive noise, and TCN [9]. In mechanical systems, [PERSON] al. introduced TCN with a residual self-attention mechanism for remaining useful life prediction [10]. For video segmentation, [PERSON] et al. proposed coarse to fine multi-resolution encoder-decoder TCN to ensure smoothness and temporal coherency [11]. [PERSON] et al. presented densely connected TCN with a squeeze-and-excitation block and attention mechanism to increase the receptive field's size for solving the lip-reading problem in videos [12].