Introduction to Time Series Analysis with Python
A practical guide to analyzing and forecasting time series data using Python libraries like pandas, statsmodels, and Prophet.
What is Time Series Data?
Time series data consists of observations collected at regular time intervals. Examples include stock prices, weather data, and website traffic. Understanding patterns in time series data is crucial for forecasting.
Exploratory Analysis
Start by visualizing your data with matplotlib or plotly. Look for trends, seasonality, and anomalies. Use pandas for data manipulation and resampling at different frequencies.
Statistical Methods
ARIMA (AutoRegressive Integrated Moving Average) is a classic approach. Use statsmodels for implementation. The auto_arima function from pmdarima can automatically select optimal parameters.
Machine Learning Approaches
Facebook Prophet handles seasonality and holidays well. For complex patterns, consider LSTM neural networks or transformer-based models like TimesFM.
Evaluation
Use metrics like MAE, RMSE, and MAPE to evaluate forecasts. Always use time-based train/test splits — never random splits for time series data.