Time series forecasting involves analyzing data points collected over regular intervals of time.
It is used to make predictions about future values in the series, such as sales, stock prices, weather patterns.
Time series forecasting assumes that future values are dependent on past values
The first step in time series forecasting is to examine the data for trends, seasonality, and other patterns
Time series forecasting methods can be classified into two main categories: statistical methods and machine learning methods.
Statistical methods include techniques such as moving averages, exponential smoothing,etc.
Machine learning methods include algorithms like linear regression, SVM, ANN, RNN, etc.
Time series forecasting models are trained on historical data to capture the patterns and relationships in the data