Welcome to the guide on time series forecasting. This section provides an overview of the concepts, techniques, and tools involved in forecasting future values based on historical data.

Key Concepts

  • Time Series: A sequence of data points collected over time intervals.
  • Trend: The general direction in which the values of a time series move.
  • Seasonality: Recurring patterns in the data that repeat at regular intervals.
  • Cycles: Long-term oscillations in the data.

Techniques

  1. Moving Averages: Simple method to smooth out short-term fluctuations and identify long-term trends.
  2. Exponential Smoothing: More sophisticated method that assigns different weights to past observations based on their proximity to the current time.
  3. ARIMA (Autoregressive Integrated Moving Average): A model that combines autoregressive, differencing, and moving average components.

Tools

  • Python: A popular programming language with several libraries for time series forecasting, such as statsmodels and pandas.
  • R: Another programming language with robust packages for time series analysis, including forecast and xts.

Learn More

For a deeper understanding of time series forecasting, we recommend checking out our comprehensive tutorial on Time Series Forecasting.

Time Series Data Visualization