Machine Learning-Based Algorithms to Knowledge Extraction from Time Series Data: A Review
Abstract
:1. Introduction
2. Times Series Analysis
2.1. Time Series Background
- f (t) is the deterministic component;
- w (t) is the random noise component.
- Trend (T): monotonous, long-term underlying trend movement, which highlights a structural evolution of the phenomenon due to causes that act systematically on it. For this reason, it is usual to assume that the trend values are expressible through a sufficiently regular and smooth function, for example, polynomial or exponential functions. If it is assumed that the function to be used is monotonous, then the trend can be an increasing or decreasing function of the phenomenon in the analyzed period [48].
- Seasonality (S): consisting of the movements of the phenomenon during the year, which, due to the influence of climatic and social factors, tend to repeat themselves in an almost similar manner in the same period (month or quarter) of subsequent years. These fluctuations originate from climatic factors (alternation of seasons) and/or social organization [49].
- Cycle (C): originating from the occurrence of more or less favorable conditions, of expansion and contraction, of the context in which the phenomenon under examination is located. It consists of fluctuations or alternating upward and downward movements, attributable to the succession in the phenomenon considered of ascending and descending phases, generally connected with the expansion and contraction phases of the entire system. The difference with respect to seasonality, which from a numerical point of view is equal to cyclicality, is the fact that the first is in the order of years for long-term cycles, while the second is in the order of months or at most one year [50].
- Accidentality or disturbance component (E): is given by irregular or accidental movements caused by a series of circumstances, each of a negligible extent. It represents the background noise of the time series, that is, the unpredictable demand component, given by the random fluctuation of the demand values around the average value of the series. The random fluctuation is detected after removing the three regular components of the time series, that is, having isolated the average demand. It should be noted that the random component is not statistically predictable. However, if it is numerically relevant, it is possible to apply linear regression models in which several independent variables are tested on the time series with only the random component. This is useful for identifying correlations between the random component and measurable input variables for which prediction for future values is also available [51].
- is the trend component;
- is the cycle component;
- is the seasonality component;
- is the accidentality component.
2.2. Times Series Datasets
3. Machine Learning Methods for Time Series Data Analysis
3.1. Artificial Neural Network-Based Methods
- Input neurons: they receive information from the environment;
- Hidden neurons: they are used by the input neurons to communicate with the output neurons or with other hidden neurons;
- Output neurons: they emit responses to the environment.
- = connection weights;
- = bias.
3.2. Time Series Claustering Methods
- Agglomerative: if they foresee a succession of mergers of the n units, starting from the basic situation in which each constitutes a group and up to the stage (n − 1) in which a group is formed which includes them all.
- Divisive: when the set of n units, in (n − 1) steps, is divided into groups that are, at each step of the analysis, subsets of a group formed at the previous stage of analysis, and which ends with the situation in which each group is composed of a unit.
- Nonhierarchical analysis techniques can be divided into the following:
- Partitions: mutually exclusive classes, such that, for a predetermined number of groups, it is possible to classify an entity into one and only one class.
- Overlapping classes: for which it is admitted the possibility that an entity may belong to more than one class at the same time.
- Model-free approaches, that is, methods that are based only on specific characteristics of the series;
- Model-based approaches, that is, methods that are based on parametric assumptions on time series;
- Prediction-based approaches, that is, methods that are based on forecasts of time series.
3.3. Convolutional Neural Network for Time Series Data
- Sparse connectivity: a single element in the trait map is connected only to small groups of pixels.
- Parameter-sharing: the same weights are used for different groups of pixels of the main image.
- Input level: represents the set of numbers that make up the image to be analyzed by the computer.
- Convolutional level: it is the main level of the network. Its goal is to identify patterns, such as curves, angles, and squares. There are more than one, and each of them focuses on finding these characteristics in the initial image. The greater the number, the greater the complexity of the feature they can identify.
- ReLu level (rectified linear units): the objective is to cancel negative values obtained in previous levels.
- Pool level: allows you to identify if the study feature is present in the previous level.
- Fully connected level: connects all the neurons of the previous level in order to establish the various identifying classes according to a certain probability. Each class represents a possible final answer.
3.4. Recurrent Neural Network
- Cell status: represents the element that carries the information that can be modified by the gates.
- Forget gate: represents the bridge where information passes through the sigmoid function. The output values are between 0 and 1. If the value is closer to 0, it means forget the information, if it is closer to 1, it means keep the information.
- Input gate: represents the element that updates the state of the cell. The sigmoid function is still present, but in combination with the tanh function. The tanh function reduces the input values to between −1 and 1. Then the output of tanh is multiplied with the output of the sigmoid function, which will decide what information is important to keep from the tanh output.
- Output gate: represents the element that decides how the next hidden state should be, which remembers the information on previous inputs to subsequent time cells. First, the previous hidden state and the current input pass into a sigmoid function, whose output, that is, the cell state just modified, passes through the tanh function. This output is then multiplied with the sigmoid output to decide what information should be contained in the hidden state. The new cell state and the new hidden state are then carried over to the next time step.
3.5. Autoencoders Algorithms in Time Series Data Processing
3.6. Automated Features Extraction from Time Series Data
4. Conclusions
Funding
Conflicts of Interest
References
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Ciaburro, G.; Iannace, G. Machine Learning-Based Algorithms to Knowledge Extraction from Time Series Data: A Review. Data 2021, 6, 55. https://doi.org/10.3390/data6060055
Ciaburro G, Iannace G. Machine Learning-Based Algorithms to Knowledge Extraction from Time Series Data: A Review. Data. 2021; 6(6):55. https://doi.org/10.3390/data6060055
Chicago/Turabian StyleCiaburro, Giuseppe, and Gino Iannace. 2021. "Machine Learning-Based Algorithms to Knowledge Extraction from Time Series Data: A Review" Data 6, no. 6: 55. https://doi.org/10.3390/data6060055