Statistical Methods to Improve the Quality of Real-Time Drilling Data

The age of easy oil is ending, the industry started drilling in remote unconventional conditions. To help produce safer, faster, and most effective operations, the utilization of artificial intelligence and machine learning (AI/ML) has become essential. Unfortunately, due to the harsh environments of drilling and the data-transmission setup, a significant amount of the real-time data could defect. The quality and effectiveness of AI/ML models are directly related to the quality of the input data; only if the input data are good, the AI/ML generated analytical and prediction models will be good. Improving the real-time data is therefore critical to the drilling industry. The objective of this paper is to propose an automated approach using eight statistical data-quality improvement algorithms on real-time drilling data. These techniques are Kalman filtering, moving average, kernel regression, median filter, exponential smoothing, lowess, wavelet filtering, and polynomial. A dataset of +150,000 rows is fed into the algorithms, and their customizable parameters are calibrated to achieve the best improvement result. An evaluation methodology is developed based on real-time drilling data characteristics to analyze the strengths and weaknesses of each algorithm were highlighted. Based on the evaluation criteria, the best results were achieved using the exponential smoothing, median filter, and moving average. Exponential smoothing and median filter techniques improved the quality of data by removing most of the invalid data points, the moving average removed more invalid data-points but trimmed the data range.

Hybrid grey exponential smoothing approach for predicting transmission dynamics of the COVID-19 outbreak in Sri Lanka

PurposeThe Coronavirus (COVID-19) is one of the major pandemic diseases caused by a newly discovered virus that has been directly affecting the human respiratory system. Because of the gradually increasing magnitude of the COVID-19 pandemic across the world, it has been sparking emergencies and critical issues in the healthcare systems around the world. However, predicting the exact amount of daily reported new COVID cases is the most serious issue faced by governments around the world today. So, the purpose of this current study is to propose a novel hybrid grey exponential smoothing model (HGESM) to predicting transmission dynamics of the COVID-19 outbreak properly.Design/methodology/approachAs a result of the complications relates to the traditional time series approaches, the proposed HGESM model is well defined to handle exponential data patterns in multidisciplinary systems. The proposed methodology consists of two parts as double exponential smoothing and grey exponential smoothing modeling approach respectively. The empirical analysis of this study was carried out on the basis of the 3rd outbreak of Covid-19 cases in Sri Lanka, from 1st March 2021 to 15th June 2021. Out of the total 90 daily observations, the first 85% of daily confirmed cases were used during the training, and the remaining 15% of the sample.FindingsThe new proposed HGESM is highly accurate (less than 10%) with the lowest root mean square error values in one head forecasting. Moreover, mean absolute deviation accuracy testing results confirmed that the new proposed model has given more significant results than other time-series predictions with the limited samples.Originality/valueThe findings suggested that the new proposed HGESM is more suitable and effective for forecasting time series with the exponential trend in a short-term manner.

Perbandingan Additive dan Multiplicative Exponential Smoothing Terhadap Prakiraan Kualitas Udara di Banjarmasin

Kekhawatiran kesehatan manusia adalah salah satu konsekuensi penting dari rendahnya kualitas udara. Kondisi rendahnya kualitas udara setiap kota akan memberikan dampak jangka panjang seperti terjadinya pemanasan global serta efek rumah kaca antropogenik. Masalah kualitas udara biasanya terjadi pada daerah yang berada beberapa bagian negara seperti Pulau Kalimantan. Sebagai pulau terbesar ketiga di dunia, Kalimantan dapat dikatakan sebagai paru-paru dunia seperti permasalahan kabut asap yang menyelimuti Kota Banjarmasin pada 2019. Kondisi tersebut dapat mengakibatkan tingginya penderita Infeksi Saluran Pernafasan Akut (ISPA). Pengambilan keputusan oleh pemangku kepentingan perlu dikaji secara mendalam untuk mencegah hal tersebut. Salah satu upaya yang dapat dilakukan adalah prakiraan kualitas udara yang akan terjadi. Data yang didapatkan dari BMKG Kota Banjarmasin merupakan bahan awal untuk prakiraan tersebut. Prakiraan kualitas udara akan menggunakan Triple Exponential Smoothing dengan 2 jenis pemodelan yaitu additive dan multiplicative, sehingga penelitian ini bertujuan untuk melakukan prakiraan kualitas udara di Kota Banjarmasin pada tahun 2021 dan 2022 menggunakan Additive dan Multiplicative Triple Exponential Smoothing. Pada prakiraan menggunakan metode tersebut, pembobotan pada nilai konstanta α, β, γ dapat menghasilkan nilai error yang kecil. Untuk menentukan perbandingan akurasi kedua pemodelan dilakukan dengan nilai RMSE. Hasil penelitian menunjukkan bahwa kondisi kualitas udara di Banjarmasin selama 2021 dan 2022 untuk polutan CO, O3, dan PM berada pada kategori aman untuk kesehatan manusia, sedangkan untuk polutan NO2 dan SO2 dinyatakan memiliki indeks yang tinggi sehingga kualitas udara dapat membahayakan kesehatan makhluk hidup. Secara perbandingan, pemodelan multiplicative pada prakiraan CO (α = 0.5, β = 0.001, dan γ = 0.149), NO2 (α = 0.5, β = 0.024, dan γ = 0.022), dan SO2 (α = 0.5, β = 0.001, dan γ = 0.037) memiliki akurasi tinggi dan nilai error yang kecil dibandingkan dengan pemodelan additive. Sebaliknya, pemodelan additive pada O3 (α = 0.5, β = 0.001, dan γ = 0.06) dan PM (α = 0.434, β = 0.001, dan γ = 0.213) memilik akurasi tinggi dan nilai error yang rendah dibandingkan pemodelan multiplicative. Kesimpulan yang didapatkan adalah perbedaan hasil prakiraan antara pemodelan additive dan multiplicative pada prakiraan kualitas udara di Banjarmasin karena pemodelan multiplicative digunakan apabila terdapat kecenderungan atau tanda bahwa pola musiman bergantung pada ukuran data. Dengan kata lain, pola musiman membesar seiring meningkatnya ukuran data. Sedangkan model additive digunakan jika kecenderungan tersebut tidak terjadi.

Analisis Triple Exponential Smoothing untuk Peramalan Kredit yang Diberikan Bank Umum Konvensional dan Syariah

Kredit yang diberikan bank merupakan pos keuangan penting yang perlu diprediksikan jumlahnya. Hal ini dikarenakan pendapatan utama sebagian besar bank di Indonesia masih bersumber dari kredit. Salah satu teknik analisis yang dapat digunakan adalah metode triple exponential smoothing. Penelitian ini bertujuan untuk memperoleh hasil ramalan (prediksi) kredit yang diberikan pada periode mendatang bagi Bank Umum Konvensional dan Syariah. Dari hasil pengolahan data diperoleh bahwa nilai alpha sebesar 0,3 memberikan hasil ramalan sebesar Rp 8.195.603 miliar dengan akurasi tertinggi atau error terkecil berdasarkan nilai MAD dan MAPE yakni sebesar Rp 59.199 Miliar dan 0.78%. Sehingga, dapat dikatakan bahwa metode Triple Exponential Smoothing merupakan metode yang efektif dan efisien untuk meramalkan jumlah KYD Bank Umum Konvensional dan Syariah.

Forecasting Shoe Sales Volume with Exponential Smoothing Method at PT Kujang Utama Antasena - Bogor

PT Kujang Utama Antasena is a shoe industry company specifically for security. The purpose of this study is to forecast or predict sales. This study uses a quantitative method with exponential smoothing, smoothing factor/constant (?) of 0.2. In production activities, forecasting is carried out to determine the amount of demand for a product and is the first step of the production planning and control process to reduce uncertainty so that an estimate that is close to the actual situation is obtained. The exponential smoothing method is a moving average forecasting method that gives exponential or graded weights to the latest data so that the latest data will get a greater weight. In other words, the newer or more current the data, the greater the weight.

FORECASTING THE CONSUMER PRICE INDEX IN YOGYAKARTA BY USING THE DOUBLE EXPONENTIAL SMOOTHING METHOD

The Consumer Price Index (CPI) is an indicator that is often used to measure the inflation rate in an area, or can be interpreted as a comparison between the prices of a commodity package from a group of goods or services consumed by households over a certain period time. The spread of COVID-19 throughout the world affects the economy in Indonesia, especially Yogyakarta. Forecasting CPI data during the COVID-19 pandemic has the benefit of being an illustration of data collection in the CPI of D.I Yogyakarta Province in the predicted period. This is useful as a comparison with the original data at the time of data collection and publication, as well as a consideration in making policies and improving the economy. Researchers use the Double Exponential Smoothing (DES) method to predict the CPI of Yogyakarta D.I Province, which aims to determine the best forecasting model and forecasting results. This method is rarely used in research on CPI data forecasting in Yogyakarta. The data in this study are monthly data from March 2020 to August 2021. The highest CPI in Yogyakarta occurred in August 2021 at 107.21 or 107.2, while the lowest CPI in Yogyakarta occurred in April 2020 at 105.15 or 105.2. The average CPI in Yogyakarta per month is 106.1. The Mean Absolute Percentage Error (MAPE) value obtained from the DES method is 0.1308443%, so that the accuracy of the model is 99.869%. Forecasting with the DES method is quite well used in forecasting the CPI data of Yogyakarta in September 2020 - November 2021. The results of CPI forecasting in Yogyakarta using the DES method were 107.2602, 107.3104, and 107.3606 from September-November.

PERBANDINGAN METODE DOUBLE EXPONENTIAL SMOOTHING HOLT DAN FUZZY TIME SERIES CHEN UNTUK PERAMALAN HARGA PALADIUM

Palladium is one of the precious metal commodities with the best performance since 3 years ago. Palladium has many benefits, including being used in the electronics, medical, jewelry and chemical industries. The benefits of palladium in the chemical field are that it can help speed up chemical reactions, filter out toxic gases in exhaust gases, and convert the gas into safer substances, so palladium is usually used as a catalyst for cars. Forecasting is a process of processing past data and projected for future interest using several mathematical models. The model used in this study is the Double Exponential Smoothing Holt and Fuzzy Time Series Chen methods. The process of forecasting palladium prices using monthly data from January 2011 to December 2020 with the Double Exponential Smoothing Holt method and the Fuzzy Time Series Chen method will be carried out in this study to describe the performance of the two methods. Based on the results of the analysis, it can be concluded that the Double Exponential Smoothing Holt and Fuzzy Time Series Chen methods have equally good performance with sMAPE values of 6.21% for Double Exponential Smoothing Holt and 9.554% for Fuzzy Time Series Chen. Forecasting for the next 3 periods using these two methods generally produces forecasting values that are close to the actual data.