scholarly journals Adaptive Online Learning for the Autoregressive Integrated Moving Average Models

Mathematics ◽  
2021 ◽  
Vol 9 (13) ◽  
pp. 1523
Author(s):  
Weijia Shao ◽  
Lukas Friedemann Radke ◽  
Fikret Sivrikaya ◽  
Sahin Albayrak
Keyword(s):  
Online Learning ◽  
Time Series Data ◽  
Moving Average ◽  
Arima Model ◽  
Theory And Practice ◽  
Series Data ◽  
Autoregressive Integrated Moving Average ◽  
Learning Techniques ◽  
Real World Datasets ◽  
Adaptive Online Learning

This paper addresses the problem of predicting time series data using the autoregressive integrated moving average (ARIMA) model in an online manner. Existing algorithms require model selection, which is time consuming and unsuitable for the setting of online learning. Using adaptive online learning techniques, we develop algorithms for fitting ARIMA models without hyperparameters. The regret analysis and experiments on both synthetic and real-world datasets show that the performance of the proposed algorithms can be guaranteed in both theory and practice.

scholarly journals Adaptive Online Learning for Time Series Prediction

2021 ◽  
Author(s):  
Weijia Shao ◽  
Lukas Friedemann Radke ◽  
Fikret Sivrikaya ◽  
Sahin Albayrak
Keyword(s):  
Time Series ◽  
Online Learning ◽  
Time Series Data ◽  
Moving Average ◽  
Time Series Prediction ◽  
Arima Model ◽  
Series Data ◽  
Real World Datasets ◽  
Regret Bound ◽  
Adaptive Online Learning

We study the problem of predicting time series data using the autoregressive integrated moving average (ARIMA) model in an online manner. Existing algorithms require model selection, which is time consuming and inapt for the setting of online learning. Using adaptive online learning techniques, we develop algorithms for fitting ARIMA models with fewest possible hyperparameters. We analyse the regret bound of the proposed algorithms and examine their performance using experiments on both synthetic and real world datasets

scholarly journals PERAMALAN JUMLAH PENUMPANG PESAWAT TERBANG DI PINTU KEDATANGAN BANDAR UDARA INTERNASIONAL PATTIMURA AMBON DENGAN MENGGUNAKAN METODE ARIMA BOX-JENKINS

2019 ◽  
Vol 13 (3) ◽  
pp. 135-144
Author(s):  
Sasmita Hayoto ◽  
Yopi Andry Lesnussa ◽  
Henry W. M. Patty ◽  
Ronald John Djami
Keyword(s):  
Time Series ◽  
Model Selection ◽  
Time Series Data ◽  
Moving Average ◽  
Arima Model ◽  
Series Data ◽  
Autoregressive Integrated Moving Average ◽  
Significant Parameter ◽  
International Airport ◽  
Forecast Time

The Autoregressive Integrated Moving Average (ARIMA) model is often used to forecast time series data. In the era of globalization, rapidly progressing times, one of them in the field of transportation. The aircraft is one of the transportation that the residents can use to support their activities, both in business and tourism. The objective of the research is to know the forecasting of the number of passengers of airplanes at the arrival gate of Pattimura Ambon International Airport using ARIMA Box-Jenkins method. The best model selection is ARIMA (0, 1, 3) because it has significant parameter value and MSE value is smaller.

Autoregressive Integrated Moving Average Model to Predict Graduate Unemployment in Indonesia

2017 ◽  
Vol 12 (1) ◽  
pp. 43-50
Author(s):  
Umi Mahmudah
Keyword(s):  
Time Series Data ◽  
Moving Average ◽  
Arima Model ◽  
Series Data ◽  
Average Model ◽  
Autoregressive Integrated Moving Average ◽  
Moving Average Model ◽  
Forecasting Analysis ◽  
Graduate Unemployment ◽  
Point Forecast

AbstractNowadays it is getting harder for higher education graduates in finding a decent job. This study aims to predict the graduate unemployment in Indonesia by using autoregressive integrated moving average (ARIMA) model. A time series data of the graduate unemployment from 2005 to 2016 is analyzed. The results suggest that ARIMA (1,2,0) is the best model for forecasting analysis, where there is a tendency of increasing number for the next ten periods. Furthermore, the average of point forecast for the next 10 periods is about 1,266,179 while its minimum value is 1,012,861 the maximum values is 1,523,156. Overall, ARIMA (1,2,0) provides an adequate forecasting model so that there is no potential for improvement.

scholarly journals Analysis and Prediction of Hydraulic Support Load Based on Time Series Data Modeling

Geofluids ◽  
2020 ◽  
Vol 2020 ◽  
pp. 1-15
Author(s):  
Yi-Hui Pang ◽  
Hong-Bo Wang ◽  
Jian-Jian Zhao ◽  
De-Yong Shang
Keyword(s):  
Time Series ◽  
Time Series Data ◽  
Moving Average ◽  
Arima Model ◽  
Sliding Window ◽  
Series Data ◽  
Average Model ◽  
Hydraulic Support ◽  
Autoregressive Integrated Moving Average ◽  
Moving Average Model

Hydraulic support plays a key role in ground control of longwall mining. The smart prediction methods of support load are important for achieving intelligent mining. In this paper, the hydraulic support load data is decomposed into trend term, cycle term, and residual term, and it is found that the data has clear trend and period features, which can be called time series data. Based on the autoregression theory and weighted moving average method, the time series model is built to analyze the load data and predict its evolution trend, and the prediction accuracy of the sliding window model, ARIMA (Autoregressive Integrated Moving Average) model, and SARIMA (Seasonal Autoregressive Integrated Moving Average) model to the hydraulic support load under different parameters are evaluated, respectively. The results of single-point and multipoint prediction test with various sliding window values indicate that the sliding window method has no advantage in predicting the trend of the support load. The ARIMA model shows a better short-term trend prediction than the sliding window model. To some extent, increasing the length of the autoregressive term can improve the long-term prediction accuracy of the model, but it also increases the sensitivity of the model to support load fluctuation, and it is still difficult to predict the load trend in one support cycle. The SARIMA model has better prediction results than the sliding window model and the ARIMA model, which reveals the load evolution trend accurately during the whole support cycle. However, there are many external factors affecting the support load, such as overburden properties, hydraulic support moving speed, and worker’s operation. The smarter model of SARIMA considering these factors should be developed to be more suitable in predicting the hydraulic support load.

scholarly journals Forecasting of Potato Prices in India: An Application of Arima Model

2020 ◽  
Vol 65 (4) ◽  
Author(s):  
Ravi Ranjan Kumar
Keyword(s):  
West Bengal ◽  
Time Series Data ◽  
Moving Average ◽  
Arima Model ◽  
Information Criteria ◽  
Madhya Pradesh ◽  
Series Data ◽  
Market Prices ◽  
Autoregressive Integrated Moving Average ◽  
Absolute Percent Error

In the present paper, Autoregressive Integrated Moving Average (ARIMA) models developed to forecast the prices of potato using time series data of eighteen years from 2002-2019. The best models selected by comparing Akaike Information Criteria (AIC), Bayesian Information Criteria (BIC), Mean Absolute Percent Error (MAPE), and Root Mean Square Error (RMSE). The study revealed that ARIMA (1,1,2), ARIMA (2,1,1)(0,0,2)[12], ARIMA (2,1,2), ARIMA (1,1,4)(0,0,1)[12], ARIMA (1,1,1)(0,1,2)[12], ARIMA (0,1,0)(0,1,1)[12], and ARIMA (3,1,3) were the best fitted models for forecasting of price of potato for the states of Utter Pradesh, West Bengal, Madhya Pradesh, Gujarat, Punjab, Tripura and India respectively. The prices of potato in Utter Pradesh, West Bengal and India will be increasing with the first-quarter providing the highest price. The prices of potato in Madhya Pradesh and Tripura will be highest in the fourth quarter. In Punjab, the prices of potato will be increasing with the third-quarter. The forecast shows that market prices of potato in Utter Pradesh, West Bengal, Madhya Pradesh, Gujarat, Punjab, Tripura, and overall India would be ruling in the highest value of .1208 `/qt, 1812 `/qt, 1345 `/qt, 1712 `/qt, 1354 `/qt, 2636 `/qt, and 1715 `/qt respectively for the year 2020.

Prediction of Gold and Silver Prices in an Emerging Economy: Comparative Analysis of Linear, Nonlinear, Hybrid, and Ensemble Models

2019 ◽  
Vol 12 (3) ◽  
pp. 63-78
Author(s):  
Saurabh Kumar
Keyword(s):  
Time Series Data ◽  
Moving Average ◽  
Arima Model ◽  
Emerging Economy ◽  
Series Data ◽  
Ensemble Model ◽  
Ann Model ◽  
Autoregressive Integrated Moving Average ◽  
Ensemble Models ◽  
Artificial Neural Network Ann

This study compares the accuracy of different forecasting techniques for gold and silver returns in a leading emerging economy. The study employs four forecasting models: autoregressive integrated moving average (ARIMA), artificial neural network (ANN), hybrid, and ensemble models. The study takes data of more than 7 years and forecasting is carried out for different forecast horizons varying from 1- to 20-steps ahead. The results reveal that ARIMA model is the best model to predict the gold returns, whereas, the ANN model along with the ensemble model are the best to predict the silver returns. The results also indicate that there exists nonlinear patterns in the time-series data of gold and silver returns. The study has significant implications for investors, academia, and policymakers.

scholarly journals Comparing of ARIMA and RBFNN for short-term forecasting

2015 ◽  
Vol 1 (1) ◽  
pp. 15 ◽  
Author(s):  
Haviluddin Haviluddin ◽  
Ahmad Jawahir
Keyword(s):  
Time Series ◽  
Time Series Data ◽  
Moving Average ◽  
Arima Model ◽  
Series Data ◽  
Autoregressive Integrated Moving Average ◽  
Proposed Model ◽  
Short Term Forecasting ◽  
Simulated Time ◽  
Modelling And Forecasting

Based on a combination of an autoregressive integrated moving average (ARIMA) and a radial basis function neural network (RBFNN), a time-series forecasting model is proposed. The proposed model has examined using simulated time series data of tourist arrival to Indonesia recently published by BPS Indonesia. The results demonstrate that the proposed RBFNN is more competent in modelling and forecasting time series than an ARIMA model which is indicated by mean square error (MSE) values. Based on the results obtained, RBFNN model is recommended as an alternative to existing method because it has a simple structure and can produce reasonable forecasts.

scholarly journals Prediction Of Tiger Shrimp Supply Using Time Series Anlysis Method Case Study CV.Surya Perdana Benur

2021 ◽  
Vol 3 (1) ◽  
pp. 27-32
Author(s):  
Yoesril Ihza Mahendra ◽  
Natalia Damastuti
Keyword(s):  
Time Series ◽  
Time Series Data ◽  
Moving Average ◽  
Arima Model ◽  
Economic Value ◽  
Series Data ◽  
Model Parameters ◽  
Marine Animal ◽  
Autoregressive Integrated Moving Average ◽  
Tiger Shrimp

Prediction of demand for tiger shrimp buyers using data from the company CV. Surya Perdana Benur. The process is carried out with the models in the Autoregressive Integrated Moving Average method. Tiger shrimp is a marine animal that is now widely cultivated by big company in Indonesia. Tiger shrimp has important economic value, so its existence must be maintained as part of Indonesian germplasm. The problem now faced by many tiger shrimp companies is the inadequate availability of goods for consumers. This time series data method is useful for predicting the availability of goods for consumers who want to buy goods at the company CV. Surya Perdana Benur. This time series data method is useful for predicting the availability of goods for consumers who want to buy goods at the company CV. Surya Perdana Benur. Autoregressive (AR), MovingAverage (MA), and Autoregressive Integrated Moving Average (ARIMA) model and will be evaluated through Mean Absolute Percent Error (MAPE). The initial process that will be carried out after the data is processed is model identification, estimation of model parameters, residual inspection, using forecasting models if the model has been fulfilled will be evaluated using MAPE until the results come out 14875.593875 to be able to predict the next buyer demand.

Forecasting of Stock Price Using Autoregressive Integrated Moving Average Model

2019 ◽  
Vol 16 (8) ◽  
pp. 3519-3524
Author(s):  
Loh Chi Jiang ◽  
Preethi Subramanian
Keyword(s):  
New York ◽  
Stock Prices ◽  
Stock Price ◽  
Time Series Data ◽  
Stock Exchange ◽  
Moving Average ◽  
Arima Model ◽  
Stock Index ◽  
Series Data ◽  
Autoregressive Integrated Moving Average

Finance sector is highly volatile where the stock prices fluctuate rapidly and it is usually challenging to forecast. The unstable conditions and rapid changes can drastically modify the monetary value of an organization or an individual. Hence, the prediction of stock prices continues to remain as one of the sizzling and vital topics in the applications of data mining in the finance sector. This forecasting is significant as it has the potential to reduce the losses that happen mainly due to erroneous intuitions and blind investment. Moreover, the prediction of stock prices endure to increase in complexity with accumulation of more and more historical data. This paper focuses on American Stock Market (New York Stock Exchange and NASDAQ Stock Exchange). Taking into account the complexity of the prediction, this research proposes Autoregressive Integrated Moving Average (ARIMA) model for estimating the value of future stock prices. ARIMA demonstrated better results for prediction as it can handle the time series data very well which is suitable for forecasting the future stock index.

scholarly journals Forecasting the GDP per Capita for Egypt and Saudi Arabia Using ARIMA Models

2020 ◽  
Vol 11 (1) ◽  
pp. 247
Author(s):  
Noura Eissa
Keyword(s):  
Saudi Arabia ◽  
Time Series Data ◽  
Moving Average ◽  
Arima Model ◽  
Gdp Per Capita ◽  
Series Data ◽  
Autoregressive Integrated Moving Average ◽  
Annual Time Series ◽  
Per Capita ◽  
Annual Time

Annual time series data is used to forecast GDP per capita using the Box-Jenkins Autoregressive-Integrated Moving-Average (ARIMA) model for the Egyptian and Saudi Arabian economies. The fitted ARIMA model is tested for per capita GDP forecasting of Egypt and of Saudi Arabia for the next ten years. Conclusions convey that the most accurate statistical model as in previous literature that forecast GDP per capita for Egypt and for Saudi Arabia is ARIMA (1,1,2) and ARIMA (1,1,1) respectively. The diagnostic tests reveal that the two models presented individually are both stable and reliable.

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