TY - GEN
T1 - Prediction of Time Series Data with Low Latitude Features
AU - Zhang, Haoran
AU - Guo, Haifeng
AU - Yang, Donghua
AU - Li, Mengmeng
AU - Zheng, Bo
AU - Wang, Hongzhi
N1 - Publisher Copyright:
© 2023, The Author(s), under exclusive license to Springer Nature Singapore Pte Ltd.
PY - 2023
Y1 - 2023
N2 - The main purpose of this paper is to study the key technology for the prediction of time series data. It has a very wide range of applications, such as forecasting sales. Forecasting sales can be said to play an important role in company operations. Whether for saving costs or inventory scheduling, accurate prediction can save unnecessary waste. From this aspect, this paper uses a neural network to achieve the purpose of the prediction. The application of neural networks in prediction has been a long time. However, most of them have not performed much research on the structure and input of neural networks, and it is not easy to process time series data. Usually, there will be many features. However, the features of data in some scenarios are small. In this paper, we determined how to predict through low-latitude features. At first, among all the ways of preprocessing data, the paper selects a mathematical method. After that, this paper builds three models in two aspects: the input and the network structure. To improve the accuracy of the results, this paper proposes two means. One is based on the seasonal characteristics of commodities. The other is based on the prediction error, called exponential smoothing. Finally, according to the results of the experiment, we come to some conclusions.
AB - The main purpose of this paper is to study the key technology for the prediction of time series data. It has a very wide range of applications, such as forecasting sales. Forecasting sales can be said to play an important role in company operations. Whether for saving costs or inventory scheduling, accurate prediction can save unnecessary waste. From this aspect, this paper uses a neural network to achieve the purpose of the prediction. The application of neural networks in prediction has been a long time. However, most of them have not performed much research on the structure and input of neural networks, and it is not easy to process time series data. Usually, there will be many features. However, the features of data in some scenarios are small. In this paper, we determined how to predict through low-latitude features. At first, among all the ways of preprocessing data, the paper selects a mathematical method. After that, this paper builds three models in two aspects: the input and the network structure. To improve the accuracy of the results, this paper proposes two means. One is based on the seasonal characteristics of commodities. The other is based on the prediction error, called exponential smoothing. Finally, according to the results of the experiment, we come to some conclusions.
KW - Data processing
KW - Neural network
KW - Prediction model
UR - https://www.scopus.com/pages/publications/85174264276
U2 - 10.1007/978-981-99-5968-6_11
DO - 10.1007/978-981-99-5968-6_11
M3 - 会议稿件
AN - SCOPUS:85174264276
SN - 9789819959679
T3 - Communications in Computer and Information Science
SP - 145
EP - 164
BT - Data Science - 9th International Conference of Pioneering Computer Scientists, Engineers and Educators, ICPCSEE 2023, Proceedings
A2 - Yu, Zhiwen
A2 - Han, Qilong
A2 - Wang, Hongzhi
A2 - Guo, Bin
A2 - Zhou, Xiaokang
A2 - Song, Xianhua
A2 - Lu, Zeguang
PB - Springer Science and Business Media Deutschland GmbH
T2 - 9th International Conference of Pioneering Computer Scientists, Engineers and Educators, ICPCSEE 2023
Y2 - 22 September 2023 through 24 September 2023
ER -