Evaluate Models - Part 5 - Reframe Time Series Forecasting Problems

There are many ways to reframe your forecast problem that can both simplify the prediction problem and potentially expose more or different information to be modeled. A reframing can ultimately result in better and/or more robust forecasts. In this tutorial, you will discover how to reframe your time series forecast problem with Python. After completing this tutorial, you will know:

• How to reframe your time series forecast problem as an alternate regression problem.
• How to reframe your time series forecast problem as a classification prediction problem.
• How to reframe your time series forecast problem with an alternate time horizon.

A. Benefits of Reframing Your Problem

There are two potential benefits to exploring alternate framings of your time series forecast problem:

1. Simplify your problem.

2. Provide the basis for an ensemble forecast.

B. Minimum Daily Temperatures Dataset

We will use the Minimum Daily Temperatures dataset as an example. This dataset describes the minimum daily temperatures over 10 years (1981-1990) in the city Melbourne, Australia.

C. Naive Time Series Forecast

The time series can then be modeled based on some function of the lagged observations.

Temp(t + 1) = B0 + (B1 × Temp(t)) + (B2 × Temp(t − 1)) ... (Bn × Temp(t − n))

D. Regression Framings

Most time series prediction problems are regression problems, requiring the prediction of a real-valued output. Below are 5 different ways that this prediction problem could be re-phrased as an alternate regression problem: 

• Forecast the change in the minimum temperature compared to the previous day.
• Forecast the minimum temperature relative to the average from the past 14 days.
• Forecast the minimum temperature relative to the average the same month last year.
• Forecast the minimum temperature rounded to the nearest 5 degrees Celsius.
• Forecast the average minimum temperature for the next 7 days.

E. Classification Framing

Classification involves predicting categorical or label outputs (like hot and cold). Below are 5 different ways that this prediction problem can be rephrased as a classification problem:

• Forecast whether a minimum temperature will be cold, moderate, or warm.
• Forecast whether a change in minimum temperature will be small or large.
• Forecast whether the minimum temperature will be a monthly minimum or not.
• Forecast whether the minimum will be higher or lower than the minimum in the previous year.
• Forecast whether the minimum temperature will rise or fall over the next 7 days.

F. Time Horizon Framings

Another axis that can be varied is the time horizon. The time horizon is the number of time steps in the future that are being predicted. Below are 5 different ways that this prediction problem can be re-phrased as a different time horizon:

• Forecast the minimum temperature for the next 7 days.
• Forecast the minimum temperature in 30 days time.
• Forecast the average minimum temperature next month.
• Forecast the day in the next week that will have the lowest minimum temperature.
• Forecast one year of minimum temperature values.