Handling missing data in multivariate time series using a vector autoregressive model-imputation (VAR-IM) algorithm

Vector autoregressive model proposed for multivariate time series data. Neural Network, including Feed Forward Neural Network (FFNN), is the powerful tool for the nonlinear model. In autoregressive model, the input layer is the past values of the same series up to certain lag and the output layers is the current value. So, VAR-NN is proposed to predict the multivariate time series data using nonlinear approach. The optimal lag time in VAR are used as aid of selecting the input in VAR-NN. In this study we develop the soft computation tools of VAR-NN based on Graphical User Interface. In each number of neurons in hidden layer, the looping process is performed several times in order to get the best result. The best one is chosen by the least of Mean Absolute Percentage Error (MAPE) criteria. In this study, the model is applied in the two series of stock price data from Indonesia Stock Exchange. Evaluation of VAR-NN performance was based on train-validation and test-validation sample approach. Based on the empirical stock price data it can be concluded that VAR-NN yields perfect performance both in in-sample and in out-sample for non-linear function approximation. This is indicated by the MAPE value that is less than 1% .

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Modelling Inflation Sectors in Indonesia Using Vector Autoregressive (VAR)

Jurnal ILMU DASAR ◽

10.19184/jid.v20i1.7259 ◽

2019 ◽

Vol 20 (1) ◽

pp. 47

Author(s):

Alan Prahutama ◽

S. Suparti ◽

Dwi Ispriyanti ◽

Tiani Wahyu Utami

Keyword(s):

Time Series ◽

White Noise ◽

Granger Causality ◽

Autoregressive Model ◽

Vector Autoregressive Model ◽

Vector Autoregressive

Analisis time series dapat dibagi menjadi dua yaitu analisis time series univariat dan analisis time series multivariat. Analisis time series univariat salah satunya menggunakan ARIMA, sedangkan analisis time series multivariat dapat menggunakan VAR. VAR merupakan pemodelan persamaan simultan yang memiliki beberapa variabel endogen secara bersamaan. Asumsi dalam model VAR antara lain terjadi kausalitas antar variabel (kausalitas Granger), residual white noise dan berdistribusi normal multivariat. Pada paper ini, metode VAR diimplementasikan dalam memodelkan sektor-sektor Inflasi di Indonesia. Adapun sektor-sektor tersebut antara lain sektor makanan (Y1t),Sektor Makanan Jadi, Minuman, Rokok dan Tembakau (Y2), Sektor perumahan, listrik, air, gas dan bahan bakar (Y3), Sektor Sandang (Y4), Sektor Kesehatan (Y5), Sektor Pendidikan dan Olahraga (Y6), Sektor Transportasi, Komunikasi dan Jasa Keuangan (Y7). Hasilnya adalah tidak semua variabel sektor inflasi berpengaruh terhadap sektor lainnya. Hanya beberapa variabel yang berpengaruh terhadap suatu sektor. Asumsi kausalitas Granger tidak semua dipenuhi oleh semua variabel. Begitu juga dengan normal multivariat juga tidak terpenuhi. Akan tetapi residual model sudah white noise. Keywords: vector autoregressive model, sectors of inflation, Granger Causality.

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Use of Vector Autoregressive Model for Anomaly Detection in Utility Gas Turbines

Volume 3: Coal, Biomass, Hydrogen, and Alternative Fuels; Cycle Innovations; Electric Power; Industrial and Cogeneration; Organic Rankine Cycle Power Systems ◽

10.1115/gt2019-90995 ◽

2019 ◽

Author(s):

Vipul Goyal ◽

Mengyu Xu ◽

Jayanta Kapat

Keyword(s):

Time Series ◽

Anomaly Detection ◽

Autoregressive Model ◽

Gas Turbines ◽

Time Series Data ◽

Combined Cycle ◽

Series Data ◽

Vector Autoregressive Model ◽

Vector Autoregressive ◽

Multi Stage

Abstract This study is based on time-series data from the combined cycle utility gas turbines consisting of three-gas turbine units and one steam turbine unit. We construct a multi-stage vector autoregressive model for the nominal operation of powerplant assuming sparsity in the association among variables and use this as a basis for anomaly detection and prediction. This prediction is compared with the time-series data of the plant-operation containing anomalies. Granger causality networks, which are based on the associations between the time series streams, are learned as an important implication from the vector autoregressive modelling. Anomaly is detected by comparing the observed measurements against their predicted value.

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The Study on Digital Marketing Influences on Sales for B2B Start-Ups in South Asia

Journal of Open Innovation Technology Market and Complexity ◽

10.3390/joitmc8010023 ◽

2022 ◽

Vol 8 (1) ◽

pp. 23

Author(s):

Iqbal Thonse Hawaldar ◽

Mithun S. Ullal ◽

Adel Sarea ◽

Rajesha T. Mathukutti ◽

Nympha Joseph

Keyword(s):

Social Media ◽

South Asia ◽

Digital Media ◽

Autoregressive Model ◽

Multivariate Time Series ◽

Digital Marketing ◽

Vector Autoregressive Model ◽

Digital Revolution ◽

Vector Autoregressive ◽

Start Ups

South Asia has seen a digital revolution in recent years. The number of persons who use the internet has risen drastically. They use it for shopping, social media and online sales. However, there exists a literature gap as far as the effect of outbound digital marketing in B2B markets is concerned. The research builds a model based on brand and consumer interactions in Indian B2B markets using a vector autoregressive model to systemically analyze the cost and outcome of digital marketing efforts by the start-ups operating in South Asia. The multivariate time series analyzed in identifying simultaneous and consistent impacts by the start-ups. We use Vector autoregressive model as it allows us to analyse the relationship among the factors as it changes over time. The research finds evidence for the conceptual framework in South Asian markets. The results prove that sales are greatly influenced by digital media, and outbound marketing efforts, predominantly word of mouth, has a huge impact in building a brand image as it spread over in the social media platforms. It is observed that the digital marketing strategies and consumer interaction are the same across South Asia, but its effect varies from country to country within South Asia thus suggesting a need of developing a new strategy in digital marketing for B2B markets.

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PENGELUARAN PEMERINTAH DI MALUKU: ANALISIS VECTOR AUTOREGRESSIVE

Jurnal Ilmu Ekonomi Terapan ◽

10.20473/jiet.v4i1.12507 ◽

2019 ◽

Vol 4 (1) ◽

Author(s):

Jacobus Cliff Diky Rijoly

Keyword(s):

Time Series ◽

Impulse Response ◽

Response Function ◽

Autoregressive Model ◽

Impulse Response Function ◽

Variance Decomposition ◽

Vector Autoregressive Model ◽

Vector Autoregressive

Pada tahun 1999 pemerintah Indonesia mengimplementasikan peraturan mengenai otonomi daerah, dampak langsung dari implementasi ini adalah setiap provinsi harus mampu mengembangkan pembangunan ekonomi di daerahnya sendiri. Hal ini juga terjadi di Maluku, peningkatan APBD (Anggaran Pendapatan dan Belanja Daerah), yang seharusnya menjadi instrument peningkatan pertumbuahan ekonomi di Maluku. Tapi, faktanya Maluku masih menjadi daerah termiskin ke 4 di Indonesia dan memiliki tingkat pengangguran paling tinggi di Indonesia. Efektifitas realisasi anggaran di duga menjadi permasalahan utama. Sesuai dengan data BPS Maluku mayoritas dari pengeluaran pemerintah yang ada digunakan sebagai pengeluaran/ belanja rutin (83.4%) dan sisanya (29.68%) diganakan sebagai belanja/ pengeluaran Modal, yang seharusnya di gunakan untuk mendorong akselerasi pertumbuhan ekonomi.Penelitian ini menggunakan VAR (Vector Autoregressive) model, untuk mengukur efek daro pengeluaran pemerintah terhadap pertumbuhan ekonomi Maluku, data yang di gunakan dalam penelitian ini menggunakan data time series dari tahun 1997-2016 yang besumber dati BPS Maluku.Hasil penelitian menunjukan bahwa pengeluaran pemerintah di tentukan oleh berbagai variabel diantaranya variabel eksogen (Kebijakan Pemerintah Melalui Penerimaan Migas maupun Non-Migas) serta variabel endogen ( PDB dan Pembentukan Modal Tetap). Hasil lain yang menggunakan instrument Impulse Response Function dan Analisis Variance Decomposition seluruh variable dalam jangka pendek dan jangka Panjang memiliki pengaruh positif terhadap Pengeluaran Pemerintah di Maluku.

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