Time Series Forecasting (TSF) Using Various Deep Learning Models

04/23/2022
by   Jimeng Shi, et al.
0

Time Series Forecasting (TSF) is used to predict the target variables at a future time point based on the learning from previous time points. To keep the problem tractable, learning methods use data from a fixed length window in the past as an explicit input. In this paper, we study how the performance of predictive models change as a function of different look-back window sizes and different amounts of time to predict into the future. We also consider the performance of the recent attention-based Transformer models, which has had good success in the image processing and natural language processing domains. In all, we compare four different deep learning methods (RNN, LSTM, GRU, and Transformer) along with a baseline method. The dataset (hourly) we used is the Beijing Air Quality Dataset from the UCI website, which includes a multivariate time series of many factors measured on an hourly basis for a period of 5 years (2010-14). For each model, we also report on the relationship between the performance and the look-back window sizes and the number of predicted time points into the future. Our experiments suggest that Transformer models have the best performance with the lowest Mean Average Errors (MAE = 14.599, 23.273) and Root Mean Square Errors (RSME = 23.573, 38.131) for most of our single-step and multi-steps predictions. The best size for the look-back window to predict 1 hour into the future appears to be one day, while 2 or 4 days perform the best to predict 3 hours into the future.

READ FULL TEXT
research
03/26/2021

Gated Transformer Networks for Multivariate Time Series Classification

Deep learning model (primarily convolutional networks and LSTM) for time...
research
04/10/2023

Two Steps Forward and One Behind: Rethinking Time Series Forecasting with Deep Learning

The Transformer is a highly successful deep learning model that has revo...
research
09/14/2023

The Effect of Smoothing on the Interpretation of Time Series Data: A COVID-19 Case Study

We conduct a controlled crowd-sourced experiment of COVID-19 case data v...
research
09/17/2018

Learning short-term past as predictor of human behavior in commercial buildings

This paper addresses the question of identifying the time-window in shor...
research
04/28/2021

Time Series Forecasting of New Cases and New Deaths Rate for COVID-19 using Deep Learning Methods

Covid-19 has been started in the year 2019 and imposed restrictions in m...
research
06/29/2022

Imaging the time series of one single referenced EEG electrode for Epileptic Seizures Risk Analysis

The time series captured by a single scalp electrode (plus the reference...
research
08/03/2022

How to Configure Masked Event Anomaly Detection on Software Logs?

Software Log anomaly event detection with masked event prediction has va...

Please sign up or login with your details

Forgot password? Click here to reset