Time series modelling is the study and prediction of the value of a time series variable as a function of a time-lag, trend and seasonal effects. Most often it is also used to find out things otherwise unobserved by examining its effects on other studied variables. A Time Series Model can be applied to various domains like finance, physics, engineering, nanotechnology, economics, biology, physical activity etc.
In this article, we will discuss time series modelling using FEDOT. We will learn everything about FEDOT. Also, we will study how to implement the system in Python. Let us get started!.
Lets start with what is Time series modelling and how FEDOT fits
Time series modelling is a major component of machine learning. It involves the use of statistical techniques to analyse the time-dependent data. Time series is a sequence of observations that are collected at regular intervals over a period of time. These observations are used for predicting future trends and behaviours.
Forecasting is about predicting what will happen in the future based on past and present data. This is a crucial step for decision making in business, finance, management and even government. Companies need to know how many products will sell next month or next year to plan their production accordingly.
Financial organizations want to know how much money they will earn or lose next quarter. Governments need to estimate future budget revenues and expenditures..
Algorithms are needed to analyze big data and make these forecasts, but these algorithms are very complex and expensive to develop. FEDOT proposes an alternative approach: it automates model development using the latest machine learning methods. The main idea is simple: FEDOT uses recent advances in artificial intelligence (AI) and meta-learning to automate machine learning model development process from raw data to a ready-to-use model.
FEDOT, short for Federated Distributed Open Toolkit, is an open-source software framework developed by ITMO’s specialists for automated machine learning (AutoML). The product can be used in various business areas: from healthcare to financial security.
FEDOT allows non-professionals to create algorithms that work with big data by setting just a few parameters: it will create a model and tune it using different algorithms and hyperparameters. Using FEDOT, companies can reduce the costs of creating their own AutoML systems or procuring them from third parties
Time Series Modeling Using FEDOT has several benefits:
-It is an efficient way to learn patterns in time series data
-It does not require much knowledge about statistics or programming
-It can be applied across different domains
FEDOT is a framework for data-driven pipeline design. You can use it for :
- Time series prediction and forecasting using various models (SVR, ARIMA, etc).
- Classification tasks.
- Regression tasks.
- Clustering tasks.
Consequently, it is important to know what are the advantages and challenges of time series modeling.
Advantages of Time Series Modeling :
- a relatively simple approach that can be easily explained and implemented
- works with only one variable, which makes data pre-processing easy
- several algorithms that work well with time series data are available in popular libraries such as Scikit-Learn in Python
Challenges of Time Series Modeling :
- requires a careful analysis of trends, seasonality and residual errors in the time series data to choose an appropriate model for prediction
- seasonality patterns may vary at multiple scales (daily, weekly, monthly, yearly) and need to be addressed accordingly
- several time series models have hyperparameters that need to be tuned for better performance
Usage of FEDOT :
FEDOT can be used for a variety of time-series forecasting problems. The solution may depend on the specific requirements of the business. Here are some examples:
Forecasting sales demand in different stores at the same time, but with some differences due to the location. We can use multiple time series models, one for each store.
Forecasting when a client will buy again after making their first purchase. We can use single time series model to forecast this.
FEDOT can be used to solve problems in the following areas:
- Regression: predict continuous variable(s)
- Classification: predict categorical variable(s)
- Time series forecasting: predict future values of a time-dependent variable
- Clustering: identify groups of observations with similar characteristics
- Dimensionality reduction: simplify the number of variables in a dataset while still capturing most of its information
Here are some of the top use cases for FEDOT:
- Building models that can be trained by multiple organizations that all want access to the same set of data, but don’t want their data to leave their network
- Building models that can be trained on large datasets quickly
- Building models with data privacy in mind, so the training data does not have to leave its location
Why does Fedot work so well?
Fedot works well because it uses the most popular ML algorithms, but it’s also able to control which algorithm is being used at any given moment in order to optimize the model-building process and get the highest-performing model possible.
What’s next for Fedot ?
We have a lot of plans for where we want to take Fedot next, including adding more autoML capabilities like hyperparameter optimization and active learning.
Implementation of Time series modelling using FEDOT :
FEDOT uses two ways to predict univariate time series:
1) Exponential smoothing models
2) ARIMA models
Exponential smoothing models are used when there is no trend or seasonality in your data (for example, this could be a stationary time series). When there is no trend or seasonality in your data, it means that data points are not increasing or decreasing over time, and they don’t follow any patterns such as seasons.
So you can fit an exponential smoothing model using FEDOT by running pipeline with exponential_smoothing primitive from forecasting library and selecting parameters you need (alpha, beta, gamma).
ARIMA models are used when there is trend and seasonality in your data (for example, this could be non-stationary time series).
#Installing FEDOT
!pip install fedot
#Importing modules from FEDOT
from fedot.api.main import Fedot
from fedot.core.data.data import train_test_data_setup
from fedot.core.repository.tasks import Task, TaskTypesEnum, TsForecastingParams
from fedot.core.data.data import InputData
from fedot.core.repository.dataset_types import DataTypesEnum
Here is the more documentation on FEDO. This repo has good examples of FEDO implementation.
The FEDOT model is often useful for time series modeling. One advantage of the FEDOT (and its close relative, ARIMA) models is that they can identify trends and seasonal patterns in the data. This allows you to accurately predict the long-term values of your time series, as well as smooth out some of the short-term fluctuations due to external events.
This benefit is what separates it from simple moving average models, which are only useful for viewing existing trends. Hope you liked this article at mldots.com
