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.
Time Series is a certain sequence of data observations that a system collects within specific periods of time — e.g., daily, monthly, or yearly. The specialized models are used to analyze the collected time-series data — describe and interpret them, as well as make certain assumptions based on shifts and odds in the collection. These shifts and odds may include the switch of trends, seasonal spikes in demand, certain repetitive changes or non-systematic shifts in usual patterns, etc.
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 machine learning principles basically outperform the classical time series forecasting approach. Thus, traditional methods are limited to processing only previously collected, readily available demand history. 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.
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:
How exactly does machine learning forecasting time series 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 instances 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.
RNNs 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.
It’s 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 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.
Univariate models can be used to model univariate time series forecasting 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.
Autoregression employs observations collected during previous time steps as input data for making regression equations that help predict the value 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.
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.
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.
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.
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 targets 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.
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.
(SVM) is a supervised machine learning algorithm that can be used for both classification or regression challenges. The ability of SVM to solve nonlinear regression estimation problems makes SVM quite successful in time series forecasting.
Gaussian process (GP), as one of the cornerstones of Bayesian non-parametric methods, has received extensive attention in machine learning time series prediction. 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.
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, CNN’s 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 RNN for temporal data storing.
This is one of the basic principles of deep learning that can be adapted in terms of different 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 a deep neural network to concentrate only on relevant data points among the barrage of various inputs, boosting the efficiency of NLP and Computer Vision.
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 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.
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.
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.
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.
This type of forecasting algorithm is deemed as the best out-of-the-box classifier by many. This means that it is best used at 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.
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.
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.
The more training data a system can extract from datasets, the higher predictive accuracy can be achieved. You may, however, experience a lack of seasonality/historical data for a target variable when working with ML, which limits the system’s learning capacity.
You may have to experiment a lot in attempts to achieve the highest forecasting efficiency. This is where a very knowledgeable approach to evaluating the most accurate predictive models is a must.
Only experienced niche specialists can handle ML feature engineering. You simply cannot tackle forecasting using machine learning algorithms without proper domain expertise.
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:
Machine learning forecasting is truly the next level of data-driven forecasting and predicting. And there are no reasons why a company or entrepreneur should miss out on reinforcing data analytics with the unprecedented powers of ML. Still, this field is filled with pitfalls and occasional complications that only an experienced specialist can handle. Turn to NIX United for a more in-depth take on your time series forecasting tasks, challenges, and far-reaching goals!
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