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\begin{document}

\title{A Time Series Classification Dataset Based on the Average Price of
Concrete in major Cities in China}



\author[1]{Qing Liu}%
\author[2]{Woon-Seek  Lee}%
\author[1]{Min Du}%
\affil[1]{Huainan Normal University}%
\affil[2]{Pukyong National University}%


\vspace{-1em}



  
  \date{May 27, 2024}


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\begin{abstract}
Time series classification (TSC) is an important and challenging problem
in data mining. Time series data sets are an important basis for this
research and are widely used in baseline verification of various
algorithm models. Aiming at the problem that there are few domestic data
sets and the current TSC data set is relatively old, a new data set for
TSC task is established based on the average price data of concrete in
major cities in China, which provides new data support for the research
of TSC algorithm. We made use of the data center of Oriental Fortune to
disclose the sample data of the average price of concrete from
2013-10-23 to 2021-01-20, created 730 autoregression-based series data
sets by using sliding windows of different lengths, and then selected
the appropriate sliding window length through machine learning model
verification, finally, convolutional neural network (CNN) and long and
short memory (LSTM) network, which are good at processing temporal
features, are used to verify the data and prove the validity of the
dataset. The dataset is freely available at
https://gitee.com/lq2012/tsc-dataset.%
\end{abstract}%



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