Time Series Analysis and Forecasting Methods: Analyzing time series analysis and forecasting methods for predicting future trends and patterns in sequential data
Keywords:
Prophet, ARIMAAbstract
Time series analysis and forecasting play a crucial role in various domains, including finance, economics, weather forecasting, and more. This paper provides a comprehensive overview and analysis of time series analysis and forecasting methods. We discuss the importance of time series analysis, the characteristics of time series data, and the challenges involved in forecasting future trends. We then review traditional statistical methods such as ARIMA and exponential smoothing, as well as more recent machine learning approaches including LSTM networks and Prophet. We compare the strengths and weaknesses of these methods, and discuss their applications in different domains. Finally, we provide recommendations for selecting the most appropriate method based on the characteristics of the data and the forecasting requirements.
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