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Short Time Series Forecasting: Recommended Methods and Techniques

Mariel Abigail Cruz-Nájera, Mayra Guadalupe Treviño-Berrones, Mirna Patricia Ponce-Flores, Jesús David Terán-Villanueva, José Antonio Castán-Rocha, Salvador Ibarra-Martínez, Alejandro Santiago, Julio Laria-Menchaca
2022 Symmetry  
Additionally, we propose five forecasting techniques that manage the seasonal component of the time series.  ...  This paper tackles the problem of forecasting real-life crime. However, the recollected data only produced thirty-five short-sized crime time series for three urban areas.  ...  Data Availability Statement: The time series used can be found here: https://mega.nz/folder/ osFwxIia#vrnxaIFAJmSQ6_pPaFvjfQ accessed on 28 May 2022. Acknowledgments: We thank Lic.  ... 
doi:10.3390/sym14061231 fatcat:flmnugo2gjgb7n6i2fs4albf6y

Short-term time series algebraic forecasting with internal smoothing

Rita Palivonaite, Minvydas Ragulskis
2014 Neurocomputing  
A new algebraic forecasting method with internal smoothing is proposed for short-term time series prediction.  ...  Numerical experiments with an artificially generated and real-world time series are used to illustrate the potential of the proposed method.  ...  Short-term time series forecasting procedures include different techniques and models.  ... 
doi:10.1016/j.neucom.2013.08.025 fatcat:hyfgs7y7izcktdhrqok7hqtjfa

Very Short Term Time-Series Forecasting of Solar Irradiance Without Exogenous Inputs [article]

Christian A. Hans, Elin Klages
2019 arXiv   pre-print
This paper compares different forecasting methods and models to predict average values of solar irradiance with a sampling time of 15 min over a prediction horizon of up to 3 h.  ...  The methods considered only require historic solar irradiance values, the current time and geographical location, i.e., no exogenous inputs are used.  ...  Also, we wish to thank Ajay Kumar Sampathirao and Steffen Hofmann for proofreading and helpful discussions.  ... 
arXiv:1810.07066v2 fatcat:ehlb47s33ffplbvc5yswctyequ

Short Term Load Forecasting Using Artificial Neural Network & Time Series Methods

Suman Adhikari, Laxman Poudel
2020 International Journal of Engineering and Applied Sciences (IJEAS)  
The researcher presented in this works support Artificial Neural Network and Time Series Methods techniques in short term forecasting.  ...  Balaju Substation, by using artificial neural network and time series methods.  ...  for their constant help, support and recommendations.  ... 
doi:10.31873/ijeas.7.04.08 fatcat:qcsgie2oqfdmzpcdm7qwzkju44

Developing the Hybrid Forecasting Model on the Short-Term Time Series

Van-Chung Pham, Ngoc-Canh Pham
2020 ICIC Express Letters  
The problem of time series data forecast has many practical benefits, thus getting more and more attention from many people. Doing forecast on short-term time series is a practical problem today.  ...  In this paper, we proposed a new model to forecast short-term time series data by developing a hybrid model between the ANN and exponential smoothing.  ...  ES3 can be forecasted on data with seasonality and trend [1] , which allows for the forecast on short-term time series.  ... 
doi:10.24507/icicel.14.10.1017 fatcat:sywaazclhvgrjfeb4zbvosfk7u

Nearest Neighbors Time Series Forecaster Based on Phase Space Reconstruction for Short-Term Load Forecasting

Jose R. Cedeño González, Juan J. Flores, Claudio R. Fuerte-Esquivel, Boris A. Moreno-Alcaide
2020 Energies  
NNLF outperformed those other techniques and the forecasting system they currently use.  ...  The article presents a comparison between NNLF and other Machine Learning techniques: Artificial Neural Networks and Support Vector Regressors.  ...  Many techniques exist to infer this τ such as the mutual information method or obtaining the mean period (T) of the time series.  ... 
doi:10.3390/en13205309 fatcat:liexlrzigvcwzkpj7dg4xo2zm4

Prediction bands for solar energy: New short-term time series forecasting techniques

Michel Fliess, Cédric Join, Cyril Voyant
2018 Solar Energy  
Short-term forecasts and risk management for photovoltaic energy is studied via a new standpoint on time series: a result published by P. Cartier and Y.  ...  The forecasts are achieved by applying quite new estimation techniques and some extrapolation procedures where the classic concept of "seasonalities" is fundamental.  ...  Back to time series and short-term forecasts120 on the trend. Taking derivatives around such bumps and holes leads obviously136 to a wrong forecasting for a larger time horizon. 137 2.7.  ... 
doi:10.1016/j.solener.2018.03.049 fatcat:yuvcoleq7neerh7lqxdm4wzxvq

Short Term Load Forecasting Using a Neural Network Based Time Series Approach

Suci Dwijayanti, Martin Hagan
2013 2013 1st International Conference on Artificial Intelligence, Modelling and Simulation  
Acknowledgements reflect the views of the author and are not endorsed by committee members or Oklahoma State University.  ...  Kyriakides and Polycarpou [17] classify these conventional methods into three categories: time series models, regression models, and Kalman filtering based techniques.  ...  Time series models, regression models and the Kalman filter are some of the conventional methods.  ... 
doi:10.1109/aims.2013.11 fatcat:kqe7j4rvovhaxj4r75gjwx37h4

Using the Hierarchical Temporal Memory Spatial Pooler for Short-Term Forecasting of Electrical Load Time Series

E.N. Osegi
2018 Applied Computing and Informatics  
In this paper, an emerging state-of-the-art machine intelligence technique called the Hierarchical Temporal Memory (HTM) is applied to the task of short-term load forecasting (STLF).  ...  The comparative performance of HTM on several daily electrical load time series data including the Eunite competition dataset and the Polish power system dataset from 2002 to 2004 are presented.  ...  It is therefore recommended that short-term load forecasting algorithms use techniques that encourage continual learning.  ... 
doi:10.1016/j.aci.2018.09.002 fatcat:uycntxmrercnfj2wa3fib3kebm

Fuzzy Time Series Methods Applied to (In)Direct Short-Term Photovoltaic Power Forecasting

Vanessa María Serrano Ardila, Joylan Nunes Maciel, Jorge Javier Gimenez Ledesma, Oswaldo Hideo Ando Junior
2022 Energies  
In this paper we propose to perform short-term forecasting of solar PV generation using fuzzy time series (FTS).  ...  Error statistics, such as RMSE and MBE, show that the fuzzy information granular method performs better than the weighted method in GHI forecasting.  ...  Personnel (CAPES) and the Brazilian Council for Scientific and Technological Development (CNPq) for financial support.  ... 
doi:10.3390/en15030845 fatcat:rzjvkbfcxndt5g3y6r4gfvirve

Attention-Based and Time Series Models for Short-Term Forecasting of COVID-19 Spread

Jurgita Markevičiūtė, Jolita Bernatavičienė, Rūta Levulienė, Viktor Medvedev, Povilas Treigys, Julius Venskus
2022 Computers Materials & Continua  
Forecasting methods and modeling techniques are important tools for governments to manage critical situations caused by pandemics, which have negative impact on public health.  ...  To evaluate the effectiveness of the proposed attention-based method combining certain data mining algorithms and the classical ARIMA model for short-term forecasts, data on the spread of the COVID-19  ...  Acknowledgement: The authors are thankful for the high-performance computing resources provided by the Information Technology Open Access Center at the Faculty of Mathematics and Informatics of Vilnius  ... 
doi:10.32604/cmc.2022.018735 fatcat:7gg4ixr6tjgozhac5yfef25cxy

Long-Term Data Traffic Forecasting for Network Dimensioning in LTE with Short Time Series

Carolina Gijón, Matías Toril, Salvador Luna-Ramírez, María Luisa Marí-Altozano, José María Ruiz-Avilés
2021 Electronics  
However, long-term forecasting (several months horizon) performed in radio planning tools relies on short and noisy time series, thus requiring a separate analysis.  ...  Previous works have shown that deep learning outperforms classical time series analysis when predicting data traffic in cellular networks in the short term (seconds/minutes) and medium term (hours/days  ...  basis, relying on short and noisy time series.  ... 
doi:10.3390/electronics10101151 fatcat:s6af6ih3wre2lhqb5csnma7qga

Evaluation of deep learning with long short-term memory networks for time series forecasting in supply chain management

Massimo Pacella, Gabriele Papadia
2021 Procedia CIRP  
To overcome statistical complexities through analyzing time series, we approach the problem with deep learning methods.  ...  To overcome statistical complexities through analyzing time series, we approach the problem with deep learning methods.  ...  The results indicate that LSTM is a competitive method, it effectively models the non-linearity of the time series and therewith it appears being able to outperform stateof-the-art linear forecasting method  ... 
doi:10.1016/j.procir.2021.03.081 fatcat:i5xrpdezenfaxmpjubfcbdjrhy

Short-Term Streamflow Forecasting Using Hybrid Deep Learning Model Based on Grey Wolf Algorithm for Hydrological Time Series

Huseyin Cagan Kilinc, Adem Yurtsever
2022 Sustainability  
It was observed that the hybrid GWO-GRU model could be used successfully in forecasting studies.  ...  Therefore, in this study, a grey wolf algorithm (GWO)-based gated recurrent unit (GRU) hybrid model is proposed for streamflow forecasting.  ...  Over the years, data-driven forecasting has drawn attention, and many data-driven models for hydrological streamflow time series forecasting have been designed [8] .  ... 
doi:10.3390/su14063352 fatcat:kzmrsuwn6bh77hwtwwmc4wa5u4

A New Model to Short-Term Power Load Forecasting Combining Chaotic Time Series and SVM

Dongxiao Niu, Yongli Wang
2009 2009 First Asian Conference on Intelligent Information and Database Systems  
Findings show that the model is effective and highly accurate in the forecasting of short-term power load.  ...  This paper presents a model for power load forecasting using support vector machine and chaotic time series. The new model can make more accurate prediction.  ...  Beijing Municipal Commission of Education disciplinary construction and Graduate Education construction projects.  ... 
doi:10.1109/aciids.2009.22 dblp:conf/aciids/NiuW09 fatcat:4lqksymmo5cgbeidppvhe7sqia
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