This tutorial will guide you through the process of time series forecasting. We'll cover the basics, common methods, and practical examples.

What is Time Series Forecasting?

Time series forecasting is the process of predicting future values based on historical data. It is widely used in various fields, such as finance, weather forecasting, and inventory management.

Common Methods

Here are some of the most common methods used in time series forecasting:

  • ARIMA (Autoregressive Integrated Moving Average): A popular method for forecasting time series data.
  • Exponential Smoothing: A method that uses weighted averages of past observations to forecast future values.
  • LSTM (Long Short-Term Memory): A type of recurrent neural network that is effective for forecasting time series data with long-term dependencies.

Practical Example

Let's say you have a dataset of daily sales for a retail store. You want to forecast the sales for the next 30 days. You can use a time series forecasting model to make this prediction.

Time Series Data Example

Learn More

For more detailed information on time series forecasting, you can visit our Time Series Analysis page.