Linguistic Approach to Time Series Forecasting
时间序列预测的语言学方法AI 解读 ↓
← 返回论文列表与筛选
阅读说明
本页整理公开书目信息，不生成学术质量评分。AI 解读根据题名及所注明的摘要或原文片段生成，未完成全文核验，可能有误；具体假设、证明与结论请核对原文。摘要保留来源文本，未经模型改写。
DOI 首次登记不等于接收或发表日期。部分更正、评论等条目没有独立摘要；缺少可靠材料时保留待补状态。
本页为预印本版本；arXiv 收录、作者填写的期刊信息以及作者既有发表经历，都不单独证明本篇已接收。作者发表记录按 OpenAlex 身份与完整姓名、本站覆盖期刊 ISSN 及正式卷期核对，只展示已核实例，档案仍可能误合并。
引用量采用 OpenAlex 单条关联记录，不合计多个记录；关联记录可能包含正式发表版本，不等于此版本单独引用。缺失不视为零，引用指标不代表论文质量。
关注作者按书目中的完整姓名匹配，同名和姓名变体需人工辨别。相关论文先按共享细分方向和具体方法排列，再参考通用方法与发表日期。
分类依据与纠错
AI 根据所提供的论文材料归类：摘要明确以动态时间序列的短中期预测为主题，并讨论非平稳性、趋势与周期。
反馈分类问题
正在核对关联版本…
代码、数据与补充材料
正在核对对应版本的资源…
时间序列预测的语言学方法
- 研究问题
- 能否利用序列片段的重复与相似性，在较少调参和较高自动化程度下，对包含趋势、周期及非平稳性的序列进行预测？
- 方法与思路
- 作者将语言学中的 N-gram 思路用于时间序列，利用片段出现、重复及序列关联和趋势，构造短期与中期预测方法。
- 材料陈述的贡献
- 摘要称方法不要求序列平稳或预先辨识其特征，调节参数较少，适合出版动态及复杂系统多参数的自动化预测。
- 阅读关注点
- 应核对序列量化、片段匹配和预测生成规则，以及趋势与周期的处理方式、样本外评价和与常用预测方法的比较。
- 阅读边界
- 这是 arXiv 预印本，同行评审与期刊接收未核实；摘要未给误差指标、比较结果或保证，无法判断非平稳预测的边界。
原文摘要
This paper proposes methods of predicting dynamic time series (including non-stationary ones) based on a linguistic approach, namely, the study of occurrences and repetition of so-called N-grams. This approach is used in computational linguistics to create statistical translators, detect plagiarism and duplicate documents. However, the scope of application can be extended beyond linguistics by taking into account the correlations of sequences of stable word combinations, as well as trends. The proposed methods do not require a preliminary study and determination of the characteristics of time series or complex tuning of the input parameters of the forecasting model. They allow, with a high level of automation, to carry out short-term and medium-term forecasts of time series, characterized by trends and cyclicality, in particular, series of publication dynamics in content monitoring systems. Also, the proposed methods can be used to predict the values of the parameters of a large complex system with the aim of monitoring its state, when the number of such parameters is significant, and therefore a high level of automation of the forecasting process is desirable. A significant advantage of the approach is the absence of requirements for time series stationarity and a small number of tuning parameters. Further research may focus on the study of various criteria for the similarity of time series fragments, the use of nonlinear similarity criteria, the search for ways to automatically determine the rational step of quantization of the time series.