Time Series Forecasting Methods
Imagine being able to predict the future—knowing stock prices, election outcomes, or tomorrow's weather. Learn how to break down time series data into trends, seasonal patterns, and noise. You'll also explore ARIMA for forecasting and see how Python can help automate this. This text is part of the Time Series and Forecasting pathway.
Topics
- forecasting techniques
- predictive modeling
- computational tools
- mathematical modeling
- time series
- time series forecasting
- simple moving average
- SMA
- weighted moving average
- WMA
- exponential moving average
- EMA
- STL decomposition
- ARIMA model
- autoregressive models
- seasonal adjustment
- autocorrelation function
- ACF
- augmented Dickey-Fuller test
- Python
- time series analysis
- trend and seasonality
- time series smoothing techniques