What are the best practices for time series analysis?

What are the best practices for time series analysis?

Some best practices include ensuring data quality, handling missing values, and choosing the right model.

How do I handle missing values in my time series data?

You can use methods like interpolation, forward filling, or even model-based approaches to handle missing values.

What models are commonly used for time series forecasting?

Common models include ARIMA, Exponential Smoothing, and more recently, machine learning models like LSTM.

Can you explain ARIMA in simple terms?

ARIMA stands for AutoRegressive Integrated Moving Average. It combines autoregression, differencing, and moving averages to model time series data.