Find Out How to Use Machine Learning for Time Series Forecasting

The modern market pace calls for a respective competitive edge. That is if you are looking to take a sturdy, profitable position amongst business competitors in your niche. For this, you can (and should) use proper technologies that can help you stay at least one step ahead of your competition.

Data forecasting has come a long way since formidable data processing-boosting technologies such as machine learning were introduced. ML-based predictive models nowadays may consider time-dependent components — seasonality, trends, cycles, irregular components, etc. — to maximize the preciseness of data-driven predictions and forecasts.

This is called machine learning forecasting and it can be most beneficially applied across all sorts of business aspects, including sales and demand forecasting, recruiting forecasting, weather forecasting, content consumption forecasting; predictive planning and maintenance, and more. 

All the previously, recently, and currently collected data is used as input for time series forecasting where future trends, seasonal changes, irregularities, and such are elaborated based on complex math-driven algorithms. And with machine learning, time series forecasting becomes faster, more precise, and more efficient in the long run. ML has proven to help better process both structured and unstructured data flows, swiftly capturing accurate patterns within massifs of data. 

It is safe to say that time series forecasting machine learning principles basically outperform the classical time series forecasting approach. Thus, traditional methods are limited to processing only previously collected, readily available demand historical data. In turn, ML autonomously defines points of interest in the unlimited flow of data to then align them with customer data insights at hand and conduct what-if analysis. This results in particularly efficient takes on stimulating the demand in the commercial sector, for instance.

However, this intricate predictive approach is beneficially used across numerous aspects of business management and optimization in most various niches.

Applications of Machine Learning Time Series Forecasting

Pretty much any company or organization dealing with constantly generated data and the need to adapt to operational shifts and changes can use time series forecasting. Machine learning serves as the ultimate booster here, allowing to better handle:

• Stock prices forecasting — the data on the history of a stock price forecasting project is combined with the data on both regular and irregular stock market spikes and drops and can be used to gain insightful predictions of the most probable upcoming stock price shifts.
• Demand and sales forecasting — customer behavior patterns data along with inputs from the history of purchases, timeline of demand, seasonal impact, etc., enable ML models to point out the most potentially demanded products and hit the spot in the dynamic market.
• Web traffic forecasting — common data on usual traffic rates among competitor websites is bunched up with input data on traffic-related patterns in order to predict web traffic rates during certain periods.
• Climate and weather prediction — time-based data is regularly gathered from numerous interconnected weather stations worldwide, while ML techniques allow thorough analysis and interpretation for future forecasts based on statistical dynamics.
• Demographic and economic forecasting — there are tons of statistical inputs in demographics and economics, which are most efficiently used for ML-based time-series predictions. As a result, the most fitting target audience can be picked and the most efficient ways to interact with that particular TA can be elaborated.
• Scientific studies forecasting — Machine Learning and deep learning for time series forecasting accelerate the rates of polishing up and introducing scientific innovations dramatically. For instance, science data that requires an indefinite number of analytical iterations can be processed much faster with the help of patterns automated by machine learning.

19 Time Series Forecasting Machine Learning Methods

How exactly does time series forecasting machine learning work in practice? Plenty of methods have been introduced over the years. Some of them may even be deemed outdated by now.

The so-called legacy time series forecasting methods either take more time and effort to implement or bring comparatively insufficient results (or both) as opposed to more recently introduced alternatives. However, they still may fit specific goals better than other approaches.

Then, there are classical methods which are well-tried-and-tested approaches that remain the default for most time series forecasting projects and are most widely used. The great thing is that they can be efficiently reinforced with the powers of ML to achieve much better results. Topical methods, on the other hand, are methods focused on particular situations and goals that fit specific forecasting scenarios.

As in any case of working with machine learning, ML forecasting can be supervised (requiring specific data input to work) or unsupervised (ongoing, self-learning data processing mechanisms). The methods listed below can be of both natures interchangeably.

Legacy Methods of Time Series Forecasting

Recurrent Neural Network (RNN)

Recurrent Neural Networks process a time series step-by-step, maintaining an internal state from time step to time step. Neural networks are great in this application as they can learn the temporal dependence from the given data.

And considering input sequences from the temporal perspective opens horizons for more precise predictions. However, the method is considered a legacy because the “education” of neural networks can be too time-consuming.

Long Short-Term Memory (LSTM)

It’s a kind of RNN, but while maintaining RNN’s ability to learn the temporal dynamics of sequential data, LSTM can furthermore handle the vanishing and exploding gradients problem.

Thus, complex multivariate data sequences can be accurately modeled, and the a need to establish pre-specified time windows (which solves many tasks that feed-forward networks cannot solve). The downside of overly time-consuming supervised learning, however, remains.

Classic Methods of Time Series Forecasting

Multi-Layer Perceptron (MLP)

Univariate models can be used to model univariate time series prediction machine learning problems. Multivariate MLP models use multivariate data where there is more than one observation for each time step.

Then, there are multistep MLP models — there is little difference to the MLP model in predicting a vector output that represents different output variables or a vector output that represents multiple time steps of one variable. This is a very widely used method that even outperforms LSTM in certain autoregression instances.

ARIMA

Autoregression employs observations collected during previous time steps as input data for making regression equations that help predict future values to be generated at the next time step.

ARIMA or an AutoRegressive Integrated Moving Average model combines autoregression and moving average principles, making forecasts correspond to linear combinations of past variable values and forecast errors, being one of the most popular approaches due to that.

Bayesian Neural Network (BNN)

BNN models involve constructing a prior distribution and updating this distribution by conditioning on the actual data. This is particularly useful for financial data because of its volatile nature, as nonlinear time series forecasting with machine learning is enabled. BNN treats network weights or parameters as random variables, being among the most universally used models out there.

Radial Basis Functions Neural Network (RBFNN)

RBF Neural Network is based on the function approximation theory or supervised and unsupervised manner was used together. Similar to BNN, RBF models are used for forecasting nonlinear time series.

RBFNN model proves to be best for predicting daily network traffic, which makes it pretty popular among commercial forecasting applications.

Kernel regression or Generalized Regression Neural Network (GRNN)

Generalized regression neural network (GRNN) is a branch of the RBF neural network. Recent research activities in forecasting with GRNN suggest that GRNN can be a promising alternative to the traditional time series model. It has shown great ability in modeling and forecasting nonlinear time series, and it is gradually entering the lines of multipurpose, commonly used methods.

K-Nearest Neighbor Regression Neural Network (KNN)

The k-nearest neighbor (k-NN) algorithm is one of the most popular non-parametric approaches used for classification, and it has been extended to regression. KNN is a supervised machine learning method that consists of instances, features, and target components. The selection of the number of neighbors and feature selection is a daunting task.

KNN is a simple algorithm that has been effectively used in various research areas such as financial modeling, image interpolation, and visual recognition.

CART Regression Trees (CART)

The technique is aimed at producing rules that predict the value of an outcome (target) variable from known values of predictor (explanatory) variables. They show good prediction accuracy performance, but they cannot detect and adapt to change or concept drift well. This approach is certainly strong in terms of unsupervised practices, but it still lacks maturity.

Support Vector Regression (SVR)

(SVM) is a supervised machine learning algorithm that can be used for both classification and regression challenges. The ability of SVM to solve nonlinear regression estimation problems makes SVM quite successful in time series forecasting.

Gaussian Processes (GP)

Gaussian process (GP), as one of the cornerstones of Bayesian non-parametric methods, has received extensive attention in time series prediction machine learning. GP has intrinsic advantages in data modeling, given its construction in the framework of Bayesian hierarchical modeling and no requirement for a priori information of function forms in Bayesian reference.

Topical Methods of Time Series Forecasting

Convolutional Neural Network (CNN)

Although analysis of image datasets is considered their main field of application, convolutional neural networks can show even better results than RNNs in time series prediction cases involving other types of spatial data. For one thing, they learn faster, boosting the overall data processing performance. However, CNNs can also be joined with RNNs to get the best of both worlds — i.e., a CNN easily recognizes spatial data and passes it to RNNs for temporal data storage.

Attention Mechanism

This is one of the basic principles of deep learning that can be adapted in terms of different neural network time series forecasting models. In a nutshell, it mimics the human brain in terms of focusing attention on specific elements that stand out from a bunch.

This enables deep learning for time series forecasting to concentrate only on relevant data points among the barrage of various inputs, boosting the efficiency of NLP and Computer Vision.

Transformer Neural Networks

A transformer neural network is an advanced architecture focused on solving sequence-to-sequence tasks. Its main goal is also to easily handle long-range dependencies.

Such networks are quite popular in ML-based models, simplifying regression by simply customizing the loss function. This comes in more than handy when it comes to regressions.

Kaggle

Kaggle is a coding and data processing environment where efficient web traffic time series forecasting can be carried out. This is an engine with technical capabilities contributed by an extensive community of enthusiasts over the years. This makes it an efficient tool for tackling the issue of predicting future values of multiple time series.

LightGBM

This one is a widely used ML algorithm that is mostly focused on capturing complex patterns within tabular datasets. This results in quite efficient sales data predictions.

In certain instances, LightGBM outruns the classical ARIMA approach in terms of making tabular-based predictions. However, both should be applied in individual situations to make out the best.

Decision Trees

ML-based decision trees are used to classify items (products) in the database. Generated classes get dedicated multivariate time series models that help predict the future price of a certain item. Obviously, this one is best for commercial analyses.

XGBoost

This is the applied machine learning algorithm that works with tabular and structured data. In its core, lie gradient-boosted decision trees. Working with XGBoost requires one to transform time series datasets into supervised learning problems. But the results should be worth it.

AdaBoost

This type of forecasting algorithm is deemed the best out-of-the-box classifier by many. This means that it is best used for elaborating data classifications in conjunction with other efficient algorithms. For instance, when used with decision trees, it learns to outline the hardest-to-classify data instances over time.

Step-by-Step Process of Time Series Forecasting Using Machine Learning

With all the above said and done, let’s look at some common practices of forecasting using machine learning. Taking things step by step here is crucial for smooth, high-quality predictive time modeling and resulting forecasting.

Preparation Stage

• Project goal definition — start with the comprehensive outline and understanding of minor and major milestones and goals. This is where preliminary research should be carried out to most properly tackle business area specifics.
• Data gathering and exploration — continuing with thorough preparation, specific data types to be analyzed and processed must be settled. Data visualization charts and plot graphs can be used for this.
• Data preparation and time series decomposition — specialized professionals should clean all involved data, gaining valuable insights and extracting proper variables. These variables can then be used for time series decomposition.

Modeling Stage

• Forecasting models evaluation — based on all the preliminary research and prep data, different forecasting models are tested and evaluated to pick the most efficient one(s).
• Forecasting model training and performance estimation — the picked algorithms for the time series machine learning model are then optimized through cross-validation and training.

Testing Stage

• Forecasting models run on testing data with known results — a step necessary for making sure the picked algorithms do their work properly.
• Accuracy and performance optimization — the last phase of polishing up algorithms to achieve the best forecasting speed and accuracy.

Deployment Stage

• Data transformation and visualization — to integrate the resulting forecasting model(s) with the production at hand, the gathered data must be conveniently transformed and visualized for further processing.
• Forecasting models revision and improvements — time series forecasting is always iterative, meaning that multiple ongoing revisions and optimizations must be implemented to continuously improve the forecasting performance.

Key Challenges of Forecasting Time Series with Machine Learning Models

The above section should give a basic idea of how a time series forecasting project is structured and done. However, keep in mind that you may (and will) come across certain common challenges of using machine learning for time series in the process. These include the following.

Lack of Time-Related Data

The more training data a system can extract from datasets, the higher predictive accuracy can be achieved. You may, however, experience a lack of historical or seasonality data for a target variable when working with ML, which limits the system’s learning capacity.

Low Data Quality

Such initial data characteristics as sparseness, incompleteness (for example, lack of timestamps), duplicate records, etc. can significantly reduce the effectiveness of using machine and deep learning algorithms.

Acceptable Accuracy for Model Evaluation

You may have to experiment a lot in an attempt to achieve the highest forecasting efficiency. This is where a very knowledgeable approach to evaluating the most accurate predictive models is a must.

Lack of Understanding of Domain Business Processes 

Only experienced niche specialists can handle ML feature engineering. You simply cannot tackle forecasting using machine learning algorithms without proper domain expertise. 

Delay in Delivery of Fresh Historical Data

“Fresh” historical data may enter the model with a delay, which can in turn reduce the accuracy of forecasting or even make forecasts irrelevant.

Insufficient Retraining Frequency

Some models are imperfect in that the frequency of retraining is not sufficient to obtain relevant predictions. At the same time, too frequent retraining can harm the performance of the network infrastructure where this model is deployed.

In general, all these problems can be solved in a few steps. The first of them involves the formation of a single source of reliable and up-to-date data. The second implies sorting and structuring this data. In turn, the third step requires optimizing the management of the chosen machine learning model, taking into account the permissible load on the network infrastructure.

And, of course, you should understand that the implementation of such models requires a professional approach – only in this way, you will be able to get the most out of machine learning technology.

Consider NIX Your Trusted Partner

NIX United is a seasoned provider of a range of advanced IT services as a whole and in-depth data management efforts in particular. To back up our expertise, we’d like to share our experience of working on several projects related to implementing machine learning algorithms for time-series predictions.

Thus far, we have successfully contributed to the development and optimization of:

1. A heart failure prediction system that uses artificial neural networks (CNN, R-CNN) — we’ve introduced essential improvements to a unique early heart failure prediction system by decoding cumbersome DICOM files, implementing complex math transformations, and boosting MRI 4D data processing. 
2. A healthcare management platform with predictive models — another healthcare solution where we’ve contributed to analytics models’ training and history data-based algorithms building as well as building a full-on authentic platform for ML models management. The resulting solution can help predict the cost-efficiency of different treatments, mortality risks, patient condition complications, hospitalization urgency, and more.
3. A credit score prediction service with CNN, RNN, MLP, XGBoost — here, we’ve been continuously boosting the quality of credit score evaluation based on the input big data provided by a large telecom organization, implementing an intelligent system that recognizes subscribers and their behaviors. 

Final Thoughts

Machine learning forecasting is truly the next level of data-driven forecasting and predicting. There are no reasons why a company or entrepreneur should miss out on reinforcing data analytics with the unprecedented powers of a time series machine learning model.

Still, this field is filled with pitfalls and occasional complications that only an experienced specialist can handle. Turn to NIX for a more in-depth take on your time series forecasting tasks, challenges, and far-reaching goals!