An important gap in the literature is the absence of fuzzy time series (FTS) multiple-input multiple-output (MIMO) methods. To fill this gap, we present a new methodology for forecasting high-dimensional non-stationary time series called MO-ENSFTS (multiple output embedding non-stationary fuzzy...
m4: The M4 competition dataset (overview). Collection of 100k univariate series at various resolutions. wiki: The Wikipedia web traffic dataset from theKaggle competition. 145k univariate high-entropy series at a single resolution. monash: Loads theMonash Time Series Forecasting Archive. Up to ~400...
Time-Series ForecastingThe paper presents a study of deep learning-based models for forecasting future directions of car sales, and car model preferences. An open-source Kaggle multivSaxena, PreetiBahad, PritikaKamal, RajSocial Science Electronic Publishing...
Time Series Classification (TSC) involves building predictive models for a discrete target variable from ordered, real valued, attributes. Over recent years, a new set of TSC algorithms have been developed which have made significant improvement over the previous state of the art. The main focus ...
Kaggle uses cookies from Google to deliver and enhance the quality of its services and to analyze traffic. Learn more OK, Got it.Al Fath Terry · 1y ago· 2,916 views arrow_drop_up50 Copy & Edit97 more_vert Multivariate Time Series Forecasting (LSTM)Note...
Explore and run machine learning code with Kaggle Notebooks | Using data from Panama Electricity Load Forecasting
Explore and run machine learning code with Kaggle Notebooks | Using data from Seattle Burke Gilman Trail
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As a substantial amount of multivariate time series data is being produced by the complex systems in smart manufacturing (SM), improved anomaly detection frameworks are needed to reduce the operational risks and the monitoring burden placed on the system operators. However, building such frameworks is...
Combining CapsNet models with LSTM models was also found to be effective for use cases such as transportation network forecasting [28]. However, not much work has been done utilising the CapsNet for use on time series data, and a minimal number of approaches are applied on time series data ...