Time Series Problems and Challenges
时间序列问题和挑战
Time series data are a very important source of information. People always tried to analyze time series data to understand the nature of events. Since ancient times, mankind has begun to wonder what lies in the essence of changes in moon cycles, weather, temperature, the river water level, harvest, and so on. And the essential way is to collect time series data of certain events and try to analyze them. Time series analysis gave a necessary tool for future prediction. These days, time series analysis is being used everywhere: from marketing and finance to education, healthcare, climate research, and robotics. There are many practical and theoretical approaches to time series forecasting: mathematics, statistics, random process theory, and so on. Artificial intelligence model-based forecasting has also become a popular research tool for the past decade. We start exploring how the latest advances in deep learning can be applied to time series forecasting
时间序列数据是一种非常重要的信息来源。人们总是试图分析时间序列数据以理解事件的本质。自古以来，人类就开始好奇月亮周期、天气、温度、河流水位、收成等变化背后的本质。而最基本的方法就是收集特定事件的时间序列数据并尝试进行分析。时间序列分析为未来预测提供了必要的工具。如今，时间序列分析的应用无处不在：从市场营销和金融到教育、医疗保健、气候研究和机器人技术。时间序列预测有许多实用和理论方法：数学、统计学、随机过程理论等。过去十年，基于人工智能模型的预测也成为了一种流行的研究工具。我们开始探索如何将深度学习的最新进展应用于时间序列预测。