An Approximation to Deep Learning Touristic-Related Time Series Forecasting

被引：0

作者：

Trujillo Viedma, Daniel ^{[1
]}

Rivera Rivas, Antonio Jesus ^{[1
]}

Charte Ojeda, Francisco ^{[1
]}

del Jesus Diaz, Maria Jose ^{[1
]}

机构：

[1] Univ Jaen, Dept Comp Sci, Andalusian Res Inst Data Sci & Computat Intellige, Jaen 23071, Spain

来源：

INTELLIGENT DATA ENGINEERING AND AUTOMATED LEARNING - IDEAL 2018, PT I | 2018年 / 11314卷

关键词：

LSTM; ARIMA; Time series forecasting;

D O I：

10.1007/978-3-030-03493-1_47

中图分类号：

TP18 [人工智能理论];

学科分类号：

081104 ; 0812 ; 0835 ; 1405 ;

摘要：

Tourism is one of the biggest economic activities around the world. This means that an adequate planning of existing resources becomes crucial. Precise demand-related forecasting greatly improves this planning. Deep Learning models are showing an greatly improvement on time-series forecasting, particularly the LSTM, which is designed for this kind of tasks. This article introduces the touristic time-series forecasting using LSTM, and compares its accuracy against well known models RandomForest and ARIMA. Our results shows that new LSTM models achieve the best accuracy.

引用

页码：448 / 456

页数：9

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