Model Types

Regression, time series forecasting, and classification are three common types of machine learning models that Akkio can generate. They are generic categories that each have specific models that are used to achieve their goal. At Akkio we pick from several of these models to determine which is best for your Regression Time Series or Classification needs.

Model Categories

Lets start with a description of each of the categories.


Use to predict a continuous numerical value. A regression model learns the relationship between input variables and a continuous output variable, such as predicting a person's salary based on their years of experience, age, education level, etc. Regression models are trained using labeled data, where the correct outputs are known, and can be used for tasks such as predicting housing prices, stock prices, or the likelihood of a customer buying a product.

Time Series Forecasting

Time series forecasting predicts future values of a variable over time, given historical data. This is often used in applications such as weather forecasting, stock market analysis, and energy demand forecasting. Models are trained using historical data and aim to identify patterns or trends in the data to make accurate predictions about future values. Common algorithms used for time series forecasting include ARIMA, SARIMA, and LSTM neural networks.


Machine learning uses classification to predict a categorical label or class. A classification model learns the relationship between input variables and a discrete output variable, such as classifying an email as spam or not spam, or predicting whether a customer is likely to churn or not. Classification models are trained using labeled data and can be used for tasks such as image recognition, sentiment analysis, or fraud detection. Common algorithms used for classification include logistic regression, decision trees, random forests, support vector machines (SVM), and deep neural networks.

Specific Model Types

Many models can be used for multiple tasks. Neural Networks and Random Forest can both be used for Classification and regression for example. Below is a list of some of the models Akkio uses to give a better insight into what the platform is doing when it picks a model for your data. Akkio also has a proprietary baseline model for both forecast and prediction modeling.

Linear Regression - Regression

Linear Regression learns a linear relationship between input variables and a continuous output variable. It is a simple and widely used model that can be trained using labeled data to predict values such as the price of a house or the weight of a person based on their height.

Decision Trees - Classification/Regression

Decision Trees learn a hierarchical structure of if-then rules based on input variables. The model is trained using labeled data and can handle both categorical and continuous variables. Decision Trees are intuitive and easy to interpret, making them a popular choice for many applications.

Random Forests - Classification/Regression

Random Forests are an ensemble of decision trees. The model combines multiple decision trees to improve performance and reduce overfitting. Random Forests can handle both categorical and continuous variables and are widely used for tasks such as image classification or stock price prediction.

Neural Networks - Classification/Regression

A neural network is a type of machine learning model inspired by the structure and function of the human brain. It consists of a large number of interconnected processing nodes or neurons that work together to learn patterns in the input data and make predictions or classifications.

Dense Neural Network

A dense neural network is a type of neural network where each neuron in a layer is connected to every neuron in the previous layer. This means that the weights and biases of each neuron can affect the output of every other neuron in the network. Dense neural networks are commonly used for image and speech recognition, natural language processing, and other complex tasks.

Sparse Neural Network

A sparse neural network is a type of neural network where only a subset of the neurons are connected to each other. This means that the weights and biases of each neuron only affect a small subset of the other neurons in the network. Sparse neural networks can be more efficient than dense neural networks for certain types of tasks, such as recommendation systems or graph-based data.

Multilayer Neural Network

A multilayer neural network is a type of neural network that has more than one hidden layer. Each additional hidden layer allows the network to learn more complex patterns in the input data, and can improve the accuracy of the predictions or classifications. However, adding more layers can also make the network more difficult to train and more prone to overfitting.

Deep Neural Network with Attention

A deep neural network with attention is a type of neural network that selectively focuses on important parts of input data during training and inference. It consists of multiple layers of neurons that gradually extract higher-level features. The attention mechanism dynamically weighs the importance of different parts of the input data at each decoding step, improving the model's accuracy and efficiency.

Exponential Smoothing - Time Series Forecasting

Exponential smoothing is a time series forecasting method that involves calculating a weighted average of past observations, with the weights decaying exponentially over time. This technique is often used to smooth out noisy time series data and make short-term predictions. There are different variations of exponential smoothing, including simple exponential smoothing, double exponential smoothing, and triple exponential smoothing.

Auto Regressive Ensemble - Time Series Forecasting

Auto Regressive Ensemble involves training multiple models on lagged versions of the same time series data. Each model makes a prediction for a future time step based on its own set of lagged inputs, and the final prediction is obtained by combining the individual predictions of all the models. This approach can improve the accuracy of time series forecasting by capturing complex dependencies between different time steps.

Auto Regressive Ensemble with Data Features

Auto Regressive Ensemble with Data Features is a variation of the Auto Regressive Ensemble method that includes additional features such as seasonal trends, weather patterns, or economic indicators, along with the lagged values of the time series data. By incorporating these external factors into the model, it can improve the accuracy of the forecasts and capture more complex relationships between the time series and the external factors.

Prophet - Time Series Forecasting

Prophet is a time series forecasting model that uses a combination of additive and multiplicative time series models with seasonality. The model is designed to handle a range of time series patterns, including trend changes, seasonal effects, and holiday effects. Prophet also allows for the inclusion of external factors such as holidays, weather, or economic indicators to improve the accuracy of the forecasts. The model is easy to use and can be trained quickly on large datasets.

Insights Reports

Each model type generates a unique Insights Report. These are detailed in this section.