Wouldn't it be great if there was a way to predict future events without all of the guesswork? Believe it or not, there is! Machine learning forecasting is a process that uses algorithms to learn from data and make predictions about future events.
Whether you want to understand your audience's needs or predict the next big trend, machine learning can help. Companies like Walmart, Under Armor, and IBM use forecasting for everything from demand prediction to forecasting price trends. Machine learning models, like neural networks, can take far more data into account, enabling more accurate predictive models.
In this article, we'll look at five reasons why machine learning is a better predictor than traditional methods.
Machine learning is a subset of artificial intelligence that’s defined as the process of teaching a computer to learn from data. It does this by identifying patterns and relationships in training data so that the computer can make predictions about future events. While traditional methods use a set of predefined rules to make predictions, machine learning is able to learn and adapt from any amount of data.
Machine learning can be used for a variety of purposes, such as predicting consumer behavior, understanding market trends, forecasting sales, or even predicting when a server might crash. In fact, it can be used for any problem where there is time-series data and a goal to predict the future.
To understand why machine learning is better for forecasting, let's first look at some traditional time series forecasting methods, like moving average, exponential smoothing, and ARIMA.
A moving average is a way of smoothing out data by calculating a weighted average of past data points. This can be helpful for eliminating noise from data and identifying trends. However, it can also be susceptible to outliers and can't account for seasonality.
Suppose we have sales data over a 5-year period:
The forecast for 2022 is $7.2 million, derived from a simple average of the last five years. However, this doesn't take into account the fact that sales are increasing each year.
Moving Average is commonly used to smooth the series of data. There are several different types of moving averages, including:
The most basic type of moving average is the Simple Moving Average (SMA), which is calculated by taking the mean of a given number of data points, past and present. The Weighted Moving Average (WMA) takes into account the relative importance of each data point, by attaching more value to recent data points.
These are commonly used in financial markets to smooth out price fluctuations and get a clearer picture of the trend.
Exponential smoothing is a method of forecasting that takes into account both past data and recent trends. It uses a weighted average to calculate a forecast, with more weight given to recent data. It’s called exponential smoothing since it assigns exponentially lower weights for older observations. This can help to eliminate the effects of outliers.
This is generally used to forecast the immediate future, and there are also several types of exponential smoothing, including:
ARIMA, which stands for Autoregressive Integrated Moving Average, is another model that uses past data to predict future events. It is a more complex method that involves doing an internal regression within the same time series, instead of predicting another time series.
These traditional methods require time-consuming manual work and data engineering, which can be difficult and expensive. Machine learning, on the other hand, is able to automatically learn from data and make predictions without any human intervention.
It can handle large amounts of data easily, and can identify patterns and relationships that humans would never be able to find. An extension of ARIMA, called SARIMA (or Seasonal ARIMA), supports univariate time series data with a seasonal component.
Let's take a look at five reasons why machine learning is a better predictor than traditional methods.
One of the key advantages of machine learning is that it can identify patterns that are too complex for humans to observe. Traditional methods of forecasting are limited by the amount of data that can be processed and analyzed by humans.
For example, suppose we wanted to forecast stock market prices. Traditional methods would rely on analysts to identify patterns in the market and make predictions based on research. However, it is often difficult for humans to identify all of the factors that influence stock prices. Machine learning can analyze large amounts of data very quickly and identify patterns that are not visible to humans. This can lead to more accurate predictions than traditional methods.
Renaissance Technologies has used machine learning to great effect in this area. The company has developed machine learning algorithms that have achieved over 70% annualized returns since its inception in 1994.
Machine learning can also make predictions based on a much larger data set than traditional methods.
Consider the problem of forecasting sales. A traditional method such as trend analysis might only consider past sales data in order to make a forecast. Machine learning, on the other hand, can analyze data from social media, customer reviews, and other sources in order to make a more accurate prediction.
In addition to time series data, machine learning models can factor in supply chain data and other real-world metrics, enabling greater demand forecasting accuracy. Traditional time series analysis falls short when it comes to big data.
One of the biggest disadvantages of traditional methods of forecasting is that they are biased by human emotions and subjective opinions. This can lead to inaccurate predictions, as humans are often swayed by their personal biases and emotions. Machine learning is not as biased by human emotions or subjective opinions, which leads to more accurate predictions.
Consider the example of a company that is considering opening a new store. Traditional methods of forecasting might be biased by the personal biases of the people doing the forecasting. For example, they may be more likely to predict that the store will be successful if they are personally invested in it, regardless of the evidence. Machine learning, on the other hand, would not be swayed by these personal biases and would make more accurate predictions.
Of course, ML models can be biased as well, if the data used to train the models has bias. However, after ensuring that you’re using unbiased data, you can rely on cross-validation to inform you if the model you’re building is accurate.
Machine learning can also adapt to changes in the data set, whereas traditional methods can become less accurate over time. As the data set changes, machine learning will adapt its predictions accordingly. This ensures that the predictions are always accurate and up-to-date. Traditional methods, on the other hand, can become less accurate over time as the data set changes.
For instance, let's say you have a data set that consists of customer purchase data. As time goes on, the customers in this data set may change. The traditional approach would be to rebuild the forecast with the new data set, which would then produce new predictions. However, if you use machine learning, the model can automatically adapt to the new data set.
Machine learning is also less easily manipulated than traditional methods. As machine learning relies on algorithms to make predictions, it is much more difficult to manipulate the predictions than it is to manipulate the predictions made by traditional methods. This leads to more accurate predictions.
There are four main steps in the machine learning forecasting process: data gathering, data pre-processing, model training, and model evaluation.
Naturally, the first step is data gathering, since data fuels all machine learning models. Data mining refers to the process of collecting and analyzing this historical data from various sources, whether it’s scraping the web, extracting information from forms, or just relevant Excel sheets. Time series models are picky about data formatting, so there need to be clear “time steps” in the data.
Data preprocessing cleans and prepares the data for use in the machine learning algorithm. This step includes things like removing noisy data, standardizing data, feature engineering, and transforming data into a format that the algorithm can understand. Even traditional statistical methods require data pre-processing.
Traditionally, technical talent was needed to perform data pre-processing with tools like Python. However, with the advent of self-service platforms like Akkio, business users can now easily clean and prepare their data without help from IT. This has increased the adoption of machine learning forecasting in business settings.
Once the data is ready, the machine learning algorithm is trained on it. This involves selecting a model type and configuring its parameters. Once the model is trained, it is put to use by forecasting future events. The performance of the model is then evaluated by comparing its predictions against actual outcomes.
Akkio builds a number of machine learning models in the background for any given problem to maximize accuracy. Depending on the dataset, this includes decision trees, ARIMA models, long short-term memory networks, recurrent neural networks (RNNs), LSTMs, and other deep learning techniques. Various optimization techniques are deployed across these machine learning methods, enabling greater accuracy than if just one model was used.
Historically, companies would have to hire data scientists to use tools like TensorFlow and Keras to build these models, but now any non-technical business professional can build and deploy models in clicks. Data science professionals can also benefit from Akkio’s methodology with faster experimentation and deployment.
Once the problem goes beyond univariate and nonlinear problems, Akkio’s power truly shines: Anyone can build highly complex supervised learning models in moments.
Suppose we wanted to predict revenue for a company. The data pre-processing step would involve removing any noisy data, such as errors in the sales data, and standardizing the data so that all the values are of the same scale. The model training step would involve finding patterns in the data to build a model that can predict future revenue. The model evaluation step would involve comparing the predictions of the model against actual revenue outcomes.
To get started using machine learning forecasting, you’ll first connect your data to Akkio through one of our integrations, or as a simple CSV or Excel file. The data would then be cleansed and prepared for use in the machine learning algorithm. The algorithm would learn from the data to build a model that can predict future revenue. The model would then be evaluated to determine how accurate it is.
With Akkio, all you need to do is connect the data and select the column you’d like to predict, with the hard work done in the background.
Machine learning is becoming an increasingly important tool for forecasting. By understanding how it works, you can take advantage of its power to make more accurate predictions for your business.
Machine learning forecasting can make predictions about future events that are far more accurate than predictions made by humans. The key to this accuracy is the machine's ability to learn from massive amounts of data.
Machine learning can predict stock market trends, weather patterns, or even the spread of diseases. Machine learning algorithms can analyze data from social media and other sources to identify patterns of disease transmission. This allows public health officials to create detailed plans for mitigating the spread of disease.
Every-day businesses can use machine learning to improve forecasting as well. For example, a retail business could use machine learning to predict how much inventory it will need to meet customer demand. This would allow the business to avoid stockouts and lost sales.
With Akkio, it's easier than ever to build and deploy a machine learning forecasting model. Akkio's platform automates the entire machine learning process, from data preparation to model selection. This makes it possible for anyone with data and a desire to predict the future to use machine learning forecasting. See our applications page for simple tutorials, or get started right away with a free trial.