Time series analysis studies data points collected at regular intervals to identify trends, patterns, and seasonal effects over time for better understanding and decision-making.
Key components include trend (long-term direction), seasonality (repeating cycles), and noise (random variation) that affect how the data behaves over time.
Forecasting uses historical data patterns to predict future values, helping businesses plan resources, manage risks, and optimize operations with data driven insights.
Popular models include ARIMA, Exponential Smoothing, and machine learning approaches, each suited for different data characteristics and forecasting needs.
Data must be cleaned, transformed, and checked for stationarity to meet model assumptions and improve accuracy in predictions.
Metrics like Mean Absolute Error (MAE) and Root Mean Squared Error (RMSE) measure how closely forecasts match actual outcomes.
Used in finance, weather prediction, inventory control, demand forecasting, and more to anticipate future trends and outcomes effectively.
Challenges include handling non stationary data, missing values, sudden changes, and choosing the right model for complex time dependent data.