Abstract
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.
Supported by The National Key Research and Development Program of China (2020YFB1006104).
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Zhang, H., Guo, H., Yang, D., Li, M., Zheng, B., Wang, H. (2023). Prediction of Time Series Data with Low Latitude Features. In: Yu, Z., et al. Data Science. ICPCSEE 2023. Communications in Computer and Information Science, vol 1879. Springer, Singapore. https://doi.org/10.1007/978-981-99-5968-6_11
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DOI: https://doi.org/10.1007/978-981-99-5968-6_11
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