Basic Forecasting Methods For Low Powered Machines & Testing: The Pros & Cons

The good news is that there are several basic forecasting methods that can be performed even on low-powered machines. These methods are not only easy to implement but also require minimal computational resources. Let's dive into three simple methods: Simple Moving Average (SMA), Exponential Smoothing, and Linear Regression on Time Steps, including their pros and cons.

1. Simple Moving Average (SMA)

What is it?
The Simple Moving Average (SMA) is a method that calculates the average of the last nn data points. It helps smooth out data to see the underlying trends.

How to implement it: Here's a simple Python code to implement SMA:

Pros:

• Very easy to understand and implement.
• Requires very low computational power.

Cons:

• Not sensitive to recent changes in data trends.
• Requires choosing a window size which can be arbitrary.

2. Exponential Smoothing

What is it?
Exponential smoothing is a technique that applies decreasing weights to older data. It is slightly more complex than SMA but still manageable on less capable devices.

How to implement it: Below is how you can use exponential smoothing in Python:

Pros:

• Better at adapting to changes in the trend compared to SMA.
• Still relatively simple to implement and run.

Cons:

• Slightly more complex than SMA.
• Requires setting a smoothing parameter.

3. Linear Regression on Time Steps

What is it?
This method involves using time steps as a predictor for trends. It assumes a linear relationship between time and the variable you're forecasting.

How to implement it: Here’s a straightforward way to perform linear regression on time series data:

Pros:

• Easy to understand and implement.
• Good for data with a clear linear trend.

Cons:

• Only suitable for data with linear trends.
• Poor at capturing seasonal variations or more complex patterns.

To Sum Things Up...

Choosing the right forecasting method for your business doesn't have to be complicated, even if you're limited by hardware. The methods described above provide a starting point that balances simplicity, computational ease, and practical utility. Experiment with these methods to see which works best for your specific business needs and data characteristics. Remember, the goal is to make informed decisions without getting bogged down by technical complexity.