time series

简明释义

时序列

英英释义

例句

1.In climate studies, researchers use time series (时间序列) to analyze temperature changes over decades.

在气候研究中，研究人员使用时间序列来分析几十年来的温度变化。

2.Sales forecasting is commonly performed using time series (时间序列) analysis of historical sales data.

销售预测通常通过对历史销售数据的时间序列分析来进行。

3.Economists utilize time series (时间序列) data to assess economic growth and cycles.

经济学家利用时间序列数据来评估经济增长和周期。

4.The weather department analyzes time series (时间序列) of rainfall to predict future precipitation patterns.

气象部门分析降雨的时间序列以预测未来的降水模式。

5.The stock market analysis often relies on time series (时间序列) data to predict future trends.

股市分析通常依赖于时间序列数据来预测未来趋势。

作文

In the field of statistics and data analysis, the concept of time series plays a crucial role in understanding and predicting various phenomena. A time series is a sequence of data points recorded or measured at successive points in time, typically at uniform intervals. This data can represent anything from daily stock prices to monthly sales figures, and even yearly climate patterns. The primary goal of analyzing a time series is to identify trends, seasonal variations, and cyclical patterns that can inform future predictions.One of the most significant applications of time series analysis is in financial markets. Investors and analysts rely on time series data to track stock prices over time, allowing them to make informed decisions about buying or selling assets. By examining historical price movements, analysts can identify patterns that may suggest future performance. For instance, if a particular stock has consistently risen in value during a specific month over several years, investors might anticipate a similar trend in the upcoming year.Moreover, time series analysis is not limited to finance. It is widely used in various fields such as economics, meteorology, and healthcare. In economics, policymakers use time series data to analyze economic indicators like GDP growth rates and unemployment figures. By studying these indicators over time, they can make informed decisions regarding fiscal and monetary policies that aim to stabilize or stimulate the economy.In meteorology, time series data is crucial for weather forecasting. Meteorologists collect and analyze data on temperature, humidity, and precipitation over time to predict future weather conditions. For example, by examining historical weather patterns, they can determine the likelihood of a drought or heavy rainfall in a specific region.Healthcare also benefits from time series analysis, particularly in monitoring disease outbreaks. Public health officials collect data on infection rates over time to identify trends and outbreaks. By analyzing this time series data, they can implement timely interventions to control the spread of diseases.To effectively analyze a time series, several techniques are employed. One common method is decomposition, where a time series is broken down into its constituent components: trend, seasonality, and irregularity. The trend represents the long-term movement in the data, while seasonality captures regular fluctuations that occur at specific intervals. Irregularity accounts for random variations that cannot be attributed to the other two components.Another popular technique is forecasting, which involves making predictions about future values based on past data. Various models, such as ARIMA (AutoRegressive Integrated Moving Average) and exponential smoothing, are used to forecast future points in a time series. These models take into account the historical data's patterns and provide a framework for predicting future trends.In conclusion, understanding time series is essential for anyone involved in data analysis, as it offers valuable insights into trends and patterns over time. Whether in finance, economics, meteorology, or healthcare, the ability to analyze and interpret time series data is crucial for making informed decisions and predictions. As we continue to collect vast amounts of data in our increasingly digital world, the importance of time series analysis will only grow, shaping the way we understand and interact with the world around us.

在统计学和数据分析领域，时间序列的概念在理解和预测各种现象方面发挥着至关重要的作用。时间序列是指在连续的时间点上记录或测量的一系列数据点，通常是在均匀的时间间隔内。这些数据可以代表从每日股票价格到每月销售数字，甚至是年度气候模式的任何东西。分析时间序列的主要目标是识别趋势、季节性变化和周期性模式，这些都可以为未来的预测提供信息。时间序列分析最重要的应用之一是在金融市场。投资者和分析师依赖时间序列数据来追踪股票价格随时间的变化，从而做出有关买卖资产的明智决策。通过检查历史价格变动，分析师可以识别可能暗示未来表现的模式。例如，如果某只特定股票在几年的特定月份内持续升值，投资者可能会预期在即将到来的年份中出现类似的趋势。此外，时间序列分析并不限于金融。它广泛应用于经济学、气象学和医疗保健等各个领域。在经济学中，政策制定者使用时间序列数据分析经济指标，如GDP增长率和失业数字。通过研究这些指标随时间的变化，他们可以就旨在稳定或刺激经济的财政和货币政策做出明智的决策。在气象学中，时间序列数据对于天气预报至关重要。气象学家收集和分析温度、湿度和降水量等数据，以预测未来的天气条件。例如，通过检查历史天气模式，他们可以确定特定地区干旱或强降雨的可能性。医疗保健同样受益于时间序列分析，特别是在监测疾病暴发方面。公共卫生官员收集感染率随时间的变化数据，以识别趋势和暴发。通过分析这些时间序列数据，他们可以实施及时干预措施以控制疾病的传播。为了有效分析时间序列，采用了几种技术。一种常见的方法是分解，其中时间序列被分解为其组成部分：趋势、季节性和不规则性。趋势表示数据中的长期运动，而季节性捕捉在特定间隔内发生的规律波动。不规则性则占据无法归因于其他两个组成部分的随机变化。另一种流行的技术是预测，它涉及根据过去的数据对未来值进行预测。各种模型，如ARIMA（自回归积分滑动平均）和指数平滑，被用来预测时间序列中的未来点。这些模型考虑了历史数据的模式，并提供了预测未来趋势的框架。总之，理解时间序列对于任何参与数据分析的人来说都是必不可少的，因为它提供了关于随时间变化的趋势和模式的宝贵见解。无论是在金融、经济学、气象学还是医疗保健中，分析和解释时间序列数据的能力对于做出明智的决策和预测至关重要。随着我们在日益数字化的世界中继续收集大量数据，时间序列分析的重要性只会增长，塑造我们理解和与周围世界互动的方式。