scholarly journals Applying Python’s Time Series Forecasting Method in Microsoft Excel – Integration as a Business Process Supporting Tool for Small Enterprises

2021 ◽  
Vol 24 (1) ◽  
Author(s):  
Jolanta Litwin ◽  
Marcin Olech ◽  
Anna Szymusik
Keyword(s):  
Time Series ◽  
Data Analysis ◽  
Business Process ◽  
Data Exchange ◽  
Process Analysis ◽  
Value Added ◽  
Time Series Forecasting ◽  
Microsoft Excel ◽  
Business User ◽  
The Right

The paper describes the current state of research, where integration of Microsoft Excel and Python interpreter, gives the business user the right tool to solve chosen business process analysis problems like: forecasting, classification or clustering. The integration is done by using Visual Basic for Application (VBA), as well as XLWings Python’s library. Both mechanisms serve as an interfaces between MS Excel and Python to allow the data exchange between each other. Creating the suitable Graphical User Interface (GUI) in Microsoft Excel, gives the business user opportunity to select specific data analysis method available in Python’s environment and set its parameters, without Python’s programming. Running the method by Python’s interpreter can bring the results, which are hard or even impossible to obtain by using Microsoft Excel only. However, the data analysis methods stored in the Python’s script, which are available to the business user, as well as VBA source code, must be designed and implemented by the data scientist. Sample, basic integration between Microsoft Excel and Python’s interpreter is presented in the paper. To present value-added of the proposed software solution, simple case study according to time series forecasting problem is described, where forecasting errors of different methods available in the Microsoft Excel and Python are presented and discussed. The paper ends with conclusions according to the results of the current researches and suggested directions of further research.

Implementasi Analisis Proses Bisnis Sebagai Upaya Pengurangan Biaya

2019 ◽  
Vol 10 (1) ◽  
pp. 1-27
Author(s):  
Aniek Wijayanti
Keyword(s):  
Business Process ◽  
Cost Reduction ◽  
Process Analysis ◽  
Value Added ◽  
Added Value ◽  
Natural Material ◽  
Management Activity ◽  
Procurement Process ◽  
Material Procurement ◽  
The Cost

Business Process Analysis can be used to eliminate or reduce a waste cost caused by non value added activities that exist in a process. This research aims at evaluating activities carried out in the natural material procurement process in the PT XYZ, calculating the effectiveness of the process cycle, finding a way to improve the process management, and calculating the cost reduction that can achieved by activity management. A case study was the approach of this research. The researcher obtained research data throughout deep interviews with the staff who directly involved in the process, observation, and documentation of natural material procurement. The result of this study show that the effectiveness of the process cycle of natural material procurement in the factory reached as much as 87,1% for the sand material and 72% for the crushed stone. This indicates that the process still carry activities with no added value and still contain ineffective costs. Through the Business Process Mechanism, these non value added activities can be managed so that the process cycle becomes more efficient and cost effectiveness is achieved. The result of the effective cycle calculation after the management activities implementation is 100%. This means that the cost of natural material procurement process has become effective. The result of calculation of the estimated cost reduction as a result of management activity is as much as Rp249.026.635,90 per year.

Landslide data analysis using various time-series forecasting models

2020 ◽  
Vol 88 ◽  
pp. 106858 ◽  
Author(s):  
Akarsh Aggarwal ◽  
Mohammed Alshehri ◽  
Manoj Kumar ◽  
Osama Alfarraj ◽  
Purushottam Sharma ◽  
...  
Keyword(s):  
Time Series ◽  
Data Analysis ◽  
Time Series Forecasting ◽  
Forecasting Models ◽  
Landslide Data

scholarly journals CAPACITY AVAILABILITY ANALYSIS TO HP SERVER PROLIANT DL380P GEN8 AT PT. AERO SYSTEM INDONESIA

2020 ◽  
Vol 2 (1) ◽  
pp. 141-148
Author(s):  
Naufal Rizki Rinditayoga ◽  
Dewi Nusraningrum
Keyword(s):  
Time Series ◽  
Data Analysis ◽  
Moving Average ◽  
Exponential Smoothing ◽  
Time Series Forecasting ◽  
Flight Data ◽  
Availability Analysis ◽  
Capacity Limit ◽  
Single Exponential ◽  
Weighted Moving Average

There has Servers who used for Keeping some domestic flight data at Soekarno-Hatta airport and its often experience downtime or servers inconnected, because these server capacity exceeds those maximum server limit. This research aims to examine and analyze capacity from HP Proliant DL380P Gen8 server that used for domestic flight data at PT. Aero Systems Indonesia. The population here used 3 servers with research sample is 1 server, HP Proliant DL380P Gen8 server. Data analysis exert time series forecasting used comparison from Moving Average, Single Exponential Smoothing and Weighted Moving Average methods. These results which using Moving Average shows that the use of server capacity exceeds those server capacity limit with highest usage up to 3,568 GB from total available capacity of 2,930 GB, so it needs to change immediately by other server capacity which more balanced with usage at PT. Aero Systems Indonesia.

A Comparison of Machine Learning Algorithms of Big Data for Time Series Forecasting Using Python

2020 ◽  
pp. 197-218
Author(s):  
Son Nguyen ◽  
Anthony Park
Keyword(s):  
Machine Learning ◽  
Neural Networks ◽  
Time Series ◽  
Big Data ◽  
Data Analysis ◽  
Big Data Analysis ◽  
Time Series Forecasting ◽  
Machine Learning Algorithms ◽  
Time Series Models ◽  
Data Sets

This chapter compares the performances of multiple Big Data techniques applied for time series forecasting and traditional time series models on three Big Data sets. The traditional time series models, Autoregressive Integrated Moving Average (ARIMA), and exponential smoothing models are used as the baseline models against Big Data analysis methods in the machine learning. These Big Data techniques include regression trees, Support Vector Machines (SVM), Multilayer Perceptrons (MLP), Recurrent Neural Networks (RNN), and long short-term memory neural networks (LSTM). Across three time series data sets used (unemployment rate, bike rentals, and transportation), this study finds that LSTM neural networks performed the best. In conclusion, this study points out that Big Data machine learning algorithms applied in time series can outperform traditional time series models. The computations in this work are done by Python, one of the most popular open-sourced platforms for data science and Big Data analysis.

scholarly journals PENGARUH JUMLAH PERCEPATAN RESTITUSI PAJAK PERTAMBAHAN NILAI TERHADAP PENERIMAAN PAJAK PERTAMBAHAN NILAI (Studi pada Kantor Pelayanan Pajak Pratama Malang Utara)

Profit ◽  
2021 ◽  
Vol 15 (01) ◽  
pp. 120-129
Author(s):  
Astri Warih Anjarwi ◽  
Linda Kharisma
Keyword(s):  
Time Series ◽  
Data Analysis ◽  
Time Series Data ◽  
Value Added ◽  
Secondary Data ◽  
Variable Number ◽  
Series Data ◽  
Value Added Tax ◽  
Analysis Technique ◽  
The Cost

The Accelerated of Value Added Tax Restitution is Indonesian government’s policy to a preliminary refund of value added tax overpayment. The simplification or the acceleration of the provision of restitution is done without strict examination and long process, but by simple research. Accelerated restitution policy is given to the Taxpayer who fulfills certain requirements (certain amount of restitution as mentioned above), certain criteria (Taxpayers who comply) and they are low risk Taxable Entrepreneurs that determined by the Minister of Finance. The Acceleration of Value Added Tax restitution is expected to reduce the cost compliance because the provision of restitution is done without examination and it is hoped that this policy could increase cash flow and liquidity of the economy. The research’s purpose is determine to impact the number of acceleration of value added tax restitution to the acceptance of value added tax. The type of research is explanatory research with a quantitative approach. The research’s data is secondary data that obtaine from the Pratama Tax Office Malang Utara. The research’s data is time series data during the periode of April 2018 – November 2019. The data analysis technique on the research is a simple regresi linier analysis. The results of this research is variable number of acceleration restitution on value added tax impact and significant for the revenue value added tax in the Pratama Tax Office Malang Utara. The value of R Square earned is 0.374 which means that the number of accelerated restitution of value added tax has an impact on the variable revenue of value added tax is 37.4%.

scholarly journals Sistem Informasi Reservasi Hotel Pada GM. Marsaringar Balige Berbasis Android

2017 ◽  
pp. 52-57
Author(s):  
S. Artaulina Sitorus ◽  
Emerson Porman Malau
Keyword(s):  
Information System ◽  
Mobile Devices ◽  
Data Exchange ◽  
Microsoft Excel ◽  
Exchange Processes ◽  
Reservation System ◽  
The Right ◽  
A Company ◽  
The Web

GM Hotels. Marsaringar Balige is a company engaged in hospitality services. During this time the reservation system at GM Hotels. Marsaringar Balige is still manual, such as booking rooms, recording guest data, and data from each part not yet integrated such as restaurant data, ballroom, karaoke, and making reports still using Microsoft Excel office applications. One effort to improve the quality of hospitality services at GMs. Marsaringar Balige is by utilizing information technology, namely by designing an Android-based Hotel Reservation Information System. Is the right solution to overcome the problems of hotel managerial data processing needs that are dynamic and can be accessed through websites or mobile devices. Mobile devices integrated with the web are one of the right choices for processing hotel information systems. A user / guest can get information by making a request from an application that was previously installed on an android smartphone to the database. All data exchange processes are stored on the web server. And the HotelGM Reservation Information System. Android-based Marsaringar Balige is built using the PHP programming language, Eclipse is supporting software for Android mobile, and Phpmyadmin as a tool for managing Mysql database, and Text editor using Macromedia Dreamweaver8.

scholarly journals Workers' Remittances and Growth Linkages in Pakistan

2016 ◽  
Vol 2 (2) ◽  
pp. 81-92
Author(s):  
Fouzia Yasmin ◽  
Muhammad Zahir Faridi ◽  
Hina Ali ◽  
Adnan Yasin
Keyword(s):  
Time Series ◽  
Data Analysis ◽  
Time Series Data ◽  
Developed Countries ◽  
Least Square ◽  
Series Data ◽  
Estimation Technique ◽  
Least Square Estimation ◽  
Ordinary Least Square ◽  
The Right

Remittances are considered as the cash inflows to the economy and are imperative international source of revenue for most of the less developed countries (LDCs). For data analysis, the Ordinary least square estimation technique was employed to the time series data for the years 1981 to 2010. This research comes with the conclusion that level of GDP is positively associated with the worker's remittances and the findings also support the optimistic view of remittances. It is suggested that govt. should take serious steps and proper measures to utilize the workers' remittances so, that the economy will be on the right track towards the development.  

scholarly journals Peramalan Produksi Beras di Provinsi Jawa Tengah

2021 ◽  
Vol 9 (2) ◽  
pp. 114-126
Author(s):  
Muhammad Ridwan ◽  
Hari Purnomo ◽  
Nancy Oktyajati
Keyword(s):  
Time Series ◽  
Exponential Smoothing ◽  
Time Series Forecasting ◽  
Microsoft Excel ◽  
Double Exponential ◽  
Double Exponential Smoothing

Ketersediaan beras lokal perlu diprediksi untuk memenuhi kebutuhan pasokan beras di Indonesia. Jawa Tengah sebagai penghasil beras terbesar ketiga di Indonesia merupakan salah satu penopang kebutuhan beras nasional. Besarnya produksi pangan di Indonesia menjadi faktor penting dalam penentuan persediaan pangan yang tepat. Peramalan produksi beras di Jawa Tengah menjadi diperlukan untuk mengetahui kondisi pangan ke depan. Tujuan penelitian ini adalah untuk mengembangkan model peramalan produksi beras di provinsi Jawa Tengah dan mengetahui perkiraan produksi beras di Provinsi Jawa Tengah 5 tahun ke depan. Metode time series forecasting digunakan dalam penelitian ini. Data yang digunakan dalam penelitian ini adalah data hasil produksi beras dari tahun 1993 hingga tahun 2020. Dari hasil uji fungsi auto korelasi diketahui bahwa data produksi memiliki pola data tren. Metode yang digunakan dalam penelitian ini adalah metode double exponential smoothing dengan dua parameter (Holt’s Methods). Model peramalan yang optimal didapatkan dengan bantuan software solver pada Microsoft Excel. Dengan menggunakan bantuan solver Microsoft Excel diperoleh nilai konstanta optimal α sebesar 0,767 dan β sebesar 0,412 dengan nilai Mean Absolute Precentage Error sebesar 4,82%. Hasil peramalan dari tahun 2021 hingga 2025 diketahui menurun setiap tahunnya. Rata-rata penurunan produksi beras dalam 5 tahun ke depan diperkirakan sebanyak 4,4% per tahunnya. Kata kunci: beras, exponential smoothing, Jawa Tengah, peramalan

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