Time Series Forecasting

Time series forecasting is the use of a model to predict the future values of a time-based data series, using previously observed values. Time series forecasting is applied in predicting values such as stock performance, unemployment figures, profits etc. The basic steps of time series forecasting include:

1. Defining a problem statement.

2. Collecting information and data.

3. Filtering out the data to remove outliers.

4. Choosing and fitting the best model on the data.

5. Running the model to predict the values.

6. Checking the accuracy of the forecasted values to validate the model.