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Aug 1, 2021 · In this paper, we propose a multivariate time series classification method based on an alternative representation of the time series, composed of a set of 41 descriptive time series features, in order to improve the interpretability of time series and results obtained. Our proposal uses traditional classifiers over the extracted features to ...
- Francisco Javier Baldan, José Manuel Benítez
- 2021
May 7, 2017 · Technically, in time series forecasting terminology the current time (t) and future times (t+1, t+n) are forecast times and past observations (t-1, t-n) are used to make forecasts. We can see how positive and negative shifts can be used to create a new DataFrame from a time series with sequences of input and output patterns for a supervised learning problem.
Nov 1, 2021 · As such, many researchers had proposed hundreds of different algorithms to solve time series classification problems since 2015 [2]. In short, time series classification problems involve data where the order in which the series is presented is important [3]. Shuffling the order within the time series will cause important information to be lost.
- Wen Xin Cheng, Ponnuthurai N. Suganthan, Rakesh Katuwal
- 2021
Aug 27, 2020 · The first step is to split the input sequences into subsequences that can be processed by the CNN model. For example, we can first split our univariate time series data into input/output samples with four steps as input and one as output. Each sample can then be split into two sub-samples, each with two time steps.
Aug 14, 2019 · If the input and output sequences are a time series, then the problem may be referred to as multi-step time series forecasting. Multi-Step Time Series Forecasting. Given a time series of observations, predict a sequence of observations for a range of future time steps. Text Summarization. Given a document of text, predict a shorter sequence of ...
Jul 27, 2021 · Method 1 : Simple Moving Average Method (SMA) This method is more suitable for data that does not have a strong trend and seasonality components. SMA is one of the simplest forecasting method that forecasts the future value of a time series data using average of the past N observations. Here, N is the hyperparameter.
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Jun 21, 2021 · Fuzzy cognitive maps (FCM) are used to represent a time series data for classification tasks. FCM transforms time series values into a high dimensional structure from which different concepts can be extracted [30]. The presence of FCM weight matrix helps in fine tuning the quality of classification, compromising the computation cost [31], [32].
