Time Series Analysis#
Time series analysis is a statistical technique used to analyze and model time-dependent data. Time series data is a sequence of measurements taken over time, such as daily stock prices or monthly rainfall amounts. Time series analysis involves identifying and modeling patterns in time series data, such as trends, seasonality, and cyclical behavior. The goal of time series analysis is to use the information in the data to make predictions about future values of the time series. Time series analysis is used in a wide range of fields, including economics, finance, meteorology, and engineering.
Time series forecasting is the use of a model to predict future values based on previously observed values. Time series forecasting is a common technique used in a wide range of applications, including predicting stock prices, economic indicators, and energy demand. Time series forecasting models are typically trained on historical data, and they use the patterns and relationships in that data to make predictions about the future. Time series forecasting can be performed using a variety of techniques, including statistical models, machine learning algorithms, and artificial neural networks. The accuracy of time series forecasts can be improved by incorporating additional information and incorporating additional time series data into the model.