Today in class we briefly touched the topic of time series forecasting. So, I decided to go a bit deeper into the topic.
- Time series forecasting. is basically making scientific forecasts based on past time-stamped data.
- It entails creating models via historical analysis and applying them to draw conclusions and inform strategic choices in the future.
- The fact that the future outcome is totally unknown at the time of the task and can only be approximated via rigorous analysis and priors supported by data is a significant differentiator in forecasting.
- Time series forecasting is the practice of utilizing modeling and statistics to analyze time series data in order to produce predictions and assist with strategic decision-making.
- Forecasts are not always accurate, and their likelihood might vary greatly, particularly when dealing with variables in time series data that fluctuate frequently and uncontrollably.
- Still, forecasting provides information about which possible scenarios are more likely—or less likely—to materialize. Generally speaking, our estimates can be more accurate the more complete the data we have.
- There is a significant difference between forecasting and “prediction”, even though they often mean the same thing. In certain sectors of the economy, forecasting may pertain to data at a certain future point in time, whereas prediction relates to future data generally.