scholarly journals Time Series Forecasting Models: A Comprehensive Review

2020 ◽  
Vol 8 (2S12) ◽  
pp. 84-86
Keyword(s):  
Time Series ◽  
Weather Forecasting ◽  
Classical Method ◽  
Moving Average ◽  
Time Series Forecasting ◽  
Support Vector ◽  
Comprehensive Review ◽  
Traffic Flow Prediction ◽  
Moving Average Model ◽  
Artificial Neural Network Ann

This comprehensive review provides an extensive overview of the existing Time Series Forecasting technique. This survey is not restricted to any single time series analysis; it provides forecasting of time series in different areas like marketing prediction, weather forecasting, technology prediction, financial forecasting etc. In this paper, we have analyzed forecasting in some areas namely, load forecasting, wind speed forecasting, prediction of energy consumption and short-term traffic flow prediction. Various models are available for prediction among them Autoregressive Integrated Moving Average model (ARIMA) is seen as a universal mechanism, these discussed forecasting areas utilizes different models that are combined with ARIMA. Hybrid models are the combination of classical models and modern methods, like ARIMA (classical method) combines with Artificial Neural Network (ANN) as well as with Support Vector Machine (SVM) (modern models). Hybrid model’s performance is depending on the variety of data that are taken for forecasting.

Forecasting e-waste recovery scale driven by seasonal data characteristics: A decomposition-ensemble approach

2021 ◽  
pp. 0734242X2110614
Author(s):  
AKM Mohsin ◽  
Lei Hongzhen ◽  
Mohammed Masum Iqbal ◽  
Zahir Rayhan Salim ◽  
Alamgir Hossain ◽  
...  
Keyword(s):  
Time Series ◽  
Moving Average ◽  
Production Capacity ◽  
Waste Recycling ◽  
Support Vector ◽  
Waste Recovery ◽  
Decomposition Procedure ◽  
Moving Average Model ◽  
The Government ◽  
Seasonal Data

Forecasting the scale of e-waste recycling is the basis for the government to formulate the development plan of circular economy and relevant subsidy policies and enterprises to evaluate resource recovery and optimise production capacity. In this article, the CH-X12 /STL-X framework for e-waste recycling scale prediction is proposed based on the idea of ‘decomposition-integration’, considering that the seasonal data characteristics of quarterly e-waste recycling scale data may lead to large forecasting errors and inconsistent forecasting results of a traditional single model. First, the seasonal data characteristics of the time series of e-waste recovery scale are identified based on Canova–Hansen (CH) test, and then the time series suitable for seasonal decomposition is extracted with X12 or seasonal-trend decomposition procedure based on loess (STL) model for seasonal components. Then, the Holt–Winters model was used to predict the seasonal component, and the support vector regression (SVR) model was used to predict the other components. Finally, the linear sum of the prediction results of each component is used to obtain the final prediction result. The empirical results show that the proposed CH-X12/STL-X forecasting framework can better meet the modelling requirements for time-series forecasting driven by different seasonal data characteristics and has better and more stable forecasting performance than traditional single models (Holt–Winters model, seasonal autoregressive integrated moving average model and SVR model).

scholarly journals River flow time series using least squares support vector machines

2011 ◽  
Vol 15 (6) ◽  
pp. 1835-1852 ◽  
Author(s):  
R. Samsudin ◽  
P. Saad ◽  
A. Shabri
Keyword(s):  
Time Series ◽  
Least Squares ◽  
Hybrid Model ◽  
River Flow ◽  
Moving Average ◽  
Peninsular Malaysia ◽  
Support Vector ◽  
Group Method ◽  
Input Variables ◽  
Artificial Neural Network Ann

Abstract. This paper proposes a novel hybrid forecasting model known as GLSSVM, which combines the group method of data handling (GMDH) and the least squares support vector machine (LSSVM). The GMDH is used to determine the useful input variables which work as the time series forecasting for the LSSVM model. Monthly river flow data from two stations, the Selangor and Bernam rivers in Selangor state of Peninsular Malaysia were taken into consideration in the development of this hybrid model. The performance of this model was compared with the conventional artificial neural network (ANN) models, Autoregressive Integrated Moving Average (ARIMA), GMDH and LSSVM models using the long term observations of monthly river flow discharge. The root mean square error (RMSE) and coefficient of correlation (R) are used to evaluate the models' performances. In both cases, the new hybrid model has been found to provide more accurate flow forecasts compared to the other models. The results of the comparison indicate that the new hybrid model is a useful tool and a promising new method for river flow forecasting.

scholarly journals A hybrid of artificial fneural network, exponential smoothing, and ARIMA models for COVID-19 time series forecasting

2021 ◽  
Vol 16 (1) ◽  
pp. 25-35
Author(s):  
Samir K. Safi ◽  
Olajide Idris Sanusi
Keyword(s):  
Time Series ◽  
Moving Average ◽  
Arima Model ◽  
Absolute Error ◽  
Time Series Forecasting ◽  
Arima Models ◽  
Artificial Neural Network Ann ◽  
Novel Coronavirus ◽  
Two Stages ◽  
Second Wave

The Auto Regressive Integrated Moving Average (ARIMA) model seems not to easily capture the nonlinear patterns exhibited by the 2019 novel coronavirus (COVID-19) in terms of daily confirmed cases. As a result, Artificial Neural Network (ANN) and Error, Trend, and Seasonality (ETS) modeling have been successfully applied to resolve problems with nonlinear estimation. Our research suggests that it would be ideal to use a single model of ETS or ARIMA for COVID-19 time series forecasting rather than a complicated Hybrid model that combines several models. We compare the forecasting performance of these models using real, worldwide, daily COVID-19 data for the period between January 22, 2020 till June 19, and June 20 till January 2, 2021 which marks two stages, each stage indicating the first and the second wave respectively. We discuss various forecasting approaches and the criteria for choosing the best forecasting technique. The best forecasting model selected was compared using the forecasting assessment criterion known as Mean Absolute Error (MAE). The empirical results show that the ETS and ARIMA models outperform the ANN and Hybrid models. The main finding from the ETS and ARIMA models analysis indicate that the magnitude of the increase in total confirmed cases over time is declining and the percentage change in the death rate is also on the decline. Our results shows that the chosen forecaste models are consistent during the first and second wave of of the pandemic. These forecasts are encouraging as the world struggles to contain the spread of COVID-19. This may be the result of the social distancing measures mandated by governments worldwide.

Time Series Forecasting using Least Square Support Vector Machine for Canadian Lynx Data

2014 ◽  
Vol 70 (5) ◽  
Author(s):  
Shuhaida Ismail ◽  
Ani Shabri
Keyword(s):  
Neural Networks ◽  
Time Series ◽  
Support Vector Machine ◽  
Moving Average ◽  
Absolute Error ◽  
Research Area ◽  
Time Series Forecasting ◽  
Least Square ◽  
Support Vector ◽  
Promising Alternative

Time series analysis and forecasting is an active research area over the last few decades. There are various kinds of forecasting models have been developed and researchers have relied on statistical techniques to predict the future. This paper discusses the application of Least Square Support Vector Machine (LSSVM) models for Canadian Lynx forecasting. The objective of this paper is to examine the flexibility of LSSVM in time series forecasting by comparing it with other models in previous research such as Artificial Neural Networks (ANN), Auto-Regressive Integrated Moving Average (ARIMA), Feed-Forward Neural Networks (FNN), Self-Exciting Threshold Auto-Regression (SETAR), Zhang’s model, Aladang’s hybrid model and Support Vector Regression (SVR) model. The experiment results show that the LSSVM model outperforms the other models based on the criteria of Mean Absolute Error (MAE) and Mean Square Error (MSE). It also indicates that LSSVM provides a promising alternative technique in time series forecasting.

scholarly journals Food Predictive Anatomization using Time Series

2020 ◽  
Vol 9 (9) ◽  
pp. 14-17
Keyword(s):  
Time Series ◽  
Time Series Data ◽  
Weather Forecasting ◽  
Moving Average ◽  
Series Data ◽  
Market Exchange ◽  
Average Model ◽  
Moving Average Model ◽  
Auto Regressive

Time series survey and forecasting upcoming values has been a research focus past years ago. Time series analysis and predict The time-series data finds its importance in various roles of implementation such as business, stock market exchange, weather forecasting, electricity demand, cost and usage of products such as fuels, etc. In this project, a detailed survey of the various techniques applied for forecasting different method of time series datasets are provided. Moving average model and Auto-Regressive Integrated Moving Average model with a case study on food predictive analysis time series data with R software.

scholarly journals Imputation of missing data in time series by different computation methods in various data set applications

2020 ◽  
Vol 32 ◽  
pp. 03010
Author(s):  
Dhiraj Magare ◽  
Sushil Labde ◽  
Manoj Gofane ◽  
Vishwesh Vyawahare
Keyword(s):  
Time Series ◽  
Missing Values ◽  
Moving Average ◽  
Fuzzy Model ◽  
Modern Technology ◽  
Support Vector ◽  
Data Set ◽  
Imputation Methods ◽  
Technology Generation ◽  
Artificial Neural Network Ann

In a modern technology generation, big volumes of data are evolved under numerous operations compared to an earlier era. However, collection of data without missing single value, is a great challenge ahead. In practice, there are many solutions suggested to avoid the missing values in time series applications. The existing methods used in imputation and their prediction with time series, varies with applications. The existing methods mostly available for imputation are least squares support vector machine (LSSVM), autoregressive integrated moving average models (ARIMA), Artificial Neural Network (ANN), Artificial Intelligence (AI) techniques, state space models, Kalman filtering and fuzzy model. The extensive experimental application data is used to analyze these methods. In addition, a synthetic set of data can also be used to forecast missing value, which improves performance of imputation methods in time series. In this paper, predominantly used imputation methods have been listed with their fundamental computational information along with their verification on set of data mentioned.

scholarly journals Improved of Forecasting Sea Surface Temperature based on Hybrid ARIMA and Support Vector Machines Models

2021 ◽  
Vol 17 (5) ◽  
pp. 609-620
Author(s):  
Wan Imanul Aisyah Wan Mohamad Nawi ◽  
Muhamad Safiih Lola ◽  
Razak Zakariya ◽  
Nurul Hila Zainuddin ◽  
Abd. Aziz K. Abd Hamid ◽  
...  
Keyword(s):  
Time Series ◽  
Surface Temperature ◽  
Sea Surface Temperature ◽  
Moving Average ◽  
Arima Model ◽  
Nonlinear Behavior ◽  
Time Series Forecasting ◽  
Sea Surface ◽  
Support Vector ◽  
Forecast Precision

Forecasting is a very effortful task owing to its features which simultaneously contain linear and nonlinear patterns. The Autoregressive Integrated Moving Average (ARIMA) model has been one the most widely utilized linear model in time series forecasting. Unfortunately, the ARIMA model cannot effortlessly handle nonlinear patterns alone. Thus, Support Vector Machine (SVM) model is introduced to solve nonlinear behavior in the datasets with high variance and uncertainty. The purposes of this study are twofold. First, to propose a hybrid ARIMA models using SVM. Secondly, to test the effectiveness of the proposed hybrid model using sea surface temperature (SST) data. Our investigation is based on two well-known real datasets, i.e., SST (modis) and in-situ SST (hycom). Statistical measurement such as MAE, MAPE, MSE, and RMSE are carried out to investigate the efficacy of the proposed models as compared to the previous ARIMA and SVMs models. The empirical results reveal that the proposed models produce lesser MAE, MAPE, MSE, and RMSE values in comparison to the single ARIMA and SVMs models. In additional, ARIMA-SVM are much better than compared to the existing models since the forecasting values are closer to the actual value. Therefore, we conclude that the presented models can be used to generate superior predicting values in time series forecasting with a way higher forecast precision.

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