Pengaruh Tingkat Minat Penelusuran Antar Penyedia Transportasi Online di Google Trends

Transportasi online adalah transportasi umum yang penggunaannya menggunakan aplikasi berbasis android dengan tujuan yang lebih efisien dalam hal waktu tempuh dan tarif yang jauh lebih murah. Transportasi online mempunyai banyak kelebihan dibandingkan dengan transportasi konvensional. Tarif yang jauh lebih murah menjadi alasan utama jenis transportasi ini banyak diminati. Hingga saat ini, transportasi online mengalami kemajuan yang sangat pesat di Indonesia. Hampir di semua tempat, transportasi online tersebut dapat dengan mudah ditemui dan juga siap digunakan dengan cara menggunakan aplikasi berbasis android agar bisa menikmati pelayanan transportasi tersebut. Sejak diakuisisnya Uber oleh Grab sejak awal tahun 2018, hanya ada dua penyedia jasa transportasi online yang merajai pasar di Indonesia yaitu Gojek dan Grab. Keduanya saling bersaing untuk menjaring banyak konsumen dengan mengeluarkan banyak promo kepada para penggunanya. Guna melihat pengaruh tingkat minat penelusuran “Grab” terhadap pengaruh tingkat minat penelusuran “Gojek” di Google Trends maka dipergunakanlah model Fungsi Transfer. Model fungsi transfer merupakan salah satu model time series yaitu gabungan pendekatan regresi dan ARIMA pada Analisis Deret Waktu (ADW). Berdasarkan model fungsi transfer dapat diketahui jika tingkat minat penelusuran “Grab” dan “Gojek” di Google Trends tidak saling mepengaruhi satu dengan lainnya.

Bayesian analysis of mixture autoregressive models covering the complete parameter space

Mixture autoregressive (MAR) models provide a flexible way to model time series with predictive distributions which depend on the recent history of the process and are able to accommodate asymmetry and multimodality. Bayesian inference for such models offers the additional advantage of incorporating the uncertainty in the estimated models into the predictions. We introduce a new way of sampling from the posterior distribution of the parameters of MAR models which allows for covering the complete parameter space of the models, unlike previous approaches. We also propose a relabelling algorithm to deal a posteriori with label switching. We apply our new method to simulated and real datasets, discuss the accuracy and performance of our new method, as well as its advantages over previous studies. The idea of density forecasting using MCMC output is also introduced.

Evaluation of relative performance of Indian states in PV resource utilization through MPI

To participate in the global fight against climate change, India has set an aggressive target of installing 100 GW of photovoltaic (PV) energy resources by 2022. However, only about 37% of this target is achieved till date, and, presently, the set target appears to be elusive. Hence it is crucial and the need of the hour to analyze the performance of different utilities to identify the regions that need a course correction. In the present work, the relative performance of the Indian states in realizing the national target of PV installed capacity is analyzed through the Data Envelopment Analysis (DEA) model. Time series analysis of the PV sector’s growth in different Indian states over the period 2017–2021 is quantified through the Malmquist productivity index (MPI). The applied methodology revealed that Rajasthan, which has the highest PV potential and second-highest PV installed capacity, is less productive than the small hilly states and union territories, which have meager PV potential. The result provides insight into the factors contributing to the inefficiencies in the development of the PV energy sector, which will help the policymakers take necessary corrective actions to improve the states’ productivity and thereby contribute more effectively to the national goal. The work may be extended to other regions of the world to strengthen the global effort to fight climate change.

Product Promotion Prediction Model Based on Evaluation Information

This paper mainly studies the impact of evaluation information on e-commerce platform on the future of products. Through natural language processing and rating, an evaluation model based on user rating and evaluation is defined to measure product quality. Among them, evaluations are differentiated: review sentiment coefficient (R) and review length (L).The evaluation model is:D=0.3*S+0.7*( 0.3*L+0.7*R). In order to predict the future reputation of products, based on the above evaluation model, time series is used to rank the products studied. Each customer purchases the product through Markov chain model, so as to predict the probability of future word-of-mouth spread of the product. Use TOPSIS method to select monthly sales, stars and comment sentiment coefficient as indicators. The comprehensive measurement method based on text and score is determined to predict whether the product is successfully promoted.

Load Forecasting Based on LSTM Neural Network and Applicable to Loads of “Replacement of Coal with Electricity”

With the complete implementation of the “Replacement of Coal with Electricity” policy, electric loads borne by urban power systems have achieved explosive growth. The traditional load forecasting method based on “similar days” only applies to the power systems with stable load levels and fails to show adequate accuracy. Therefore, a novel load forecasting approach based on long short-term memory (LSTM) was proposed in this paper. The structure of LSTM and the procedure are introduced firstly. The following factors have been fully considered in this model: time-series characteristics of electric loads; weather, temperature, and wind force. In addition, an experimental verification was performed for “Replacement of Coal with Electricity” data. The accuracy of load forecasting was elevated from 83.2 to 95%. The results indicate that the model promptly and accurately reveals the load capacity of grid power systems in the real application, which has proved instrumental to early warning and emergency management of power system faults.

Progressive landscape transformation from a fluvial to a glacial topography

<p>Mid-latitude mountain ranges such as the Eastern Alps are characterized by a strong topographic imprint of Pleistocene glaciations. The characteristic geometry of glacial landforms has been quantified in various ways, but studies about the evolution of glacial landscape metrics are lacking. However, such information is needed to interpret the evolutionary state of glacial topography.</p><p>By employing a landscape evolution model for cold climate processes, we trace the fluvial-to-glacial transformation of a synthetic landscape. Our simulations inspired by alpine glaciations of mid-latitude mountain ranges with peaks and ridges towering above the glacier network lead to a general increase in relief. This is expressed as the formation of overdeepened valleys with steepened flanks. Overdeepening starts at the glacier front and progressively extends upstream with ongoing glacial erosion.</p><p>The topographic signature of the progressively transforming landscape is characterized by an increase of mean channel slopes and its variance. However, above the steep flanks, the initial fluvial topography is persisting. Whereas the initial fluvial mountain range is characterized by a monotonic increase of channel slope with elevation, a transition from increasing to decreasing channel slope with elevation emerges above the equilibrium line altitude where (tributary-)headwalls transition to ridges and summits. This turning point and a high slope variance becomes progressively distinctive with ongoing glacial occupation.</p><p>By comparing landscape metrics derived from model time series with those of the Eastern Alps, we found that the temporal transition observed in our numerical experiments occur as spatial transition from the fully glaciated western to a minorly glaciated eastern part of the Alps. Thus, slope-elevation plots serve as a diagnostic tool for interpreting the glacial - fluvial influence in mountain landscapes. However, catchments of the unglaciated part of the Eastern Alps show also turning points in their slope-elevation distributions, but the variance of slope is significantly smaller at all elevation levels, when compared to the glaciated part.</p>

PERAMALAN TINGKAT PENGANGGURAN DI INDONESIA MENGGUNAKAN METODE TIME SERIES DENGAN MODEL ARIMA DAN HOLT-WINTERS

Beberapa penyebab terjadinya pengangguran di Indonesia ialah, tingkat urbanisasi, tingkat industrialisasi, proporsi angkatan kerja SLTA dan upah minimum provinsi. Faktor-faktor tersebut turut serta mempengaruhi persentase data terkait tingkat pengangguran menjadi sedikit fluktuatif. Berdasarkan pergerakan persentase data tersebut, diperlukan sebuah prediksi untuk mengetahui persentase tingkat pengangguran di masa depan dengan menggunakan konsep peramalan. Pada penelitian ini, peneliti melakukan analisis peramalan time series menggunakan metode Box-Jenkins dengan model Autoregressive Integrated Moving Average (ARIMA) dan metode Exponential Smoothing dengan model Holt-Winters. Pada penelitian ini, peramalan dilakukan dengan menggunakan dataset tingkat pengangguran dari tahun 2005 hingga 2019 per 6 bulan antara Februari hingga Agustus. Peneliti akan melihat evaluasi Range Mean Square Error (RMSE) dan Mean Square Error (MSE) terkecil dari setiap model time series. Berdasarkan hasil penelitian, ARIMA(0,1,12) menjadi model yang terbaik untuk metode Box-Jenkins sedangkan Holt-Winters dengan alpha(mean) = 0.3 dan beta(trend) = 0.4 menjadi yang terbaik pada metode Exponential Smoothing. Pemilihan model terbaik dilanjutkan dengan perbandingan nilai akurasi RMSE dan MSE. Pada model ARIMA(0,1,12) nilai RMSE = 1.01 dan MSE = 1.0201, sedangkan model Holt-Winters menghasilkan nilai RMSE = 0.45 dan MSE = 0.2025. Berdasarkan data tersebut terpilih model Holt-Winters sebagai model terbaik untuk peramalan data tingkat pengangguran di Indonesia.

Peramalan Data dengan Teknik Pemulusan Simple Moving Average (Studi Kasus Harga Saham Harian PT Bank BRI Syariah Tbk)

Time series analysis is used to model time series data and forecast data for future periods. This research was conducted to predict data with a simple smoothing technique, namely the Simple Moving Average of PT Bank BRI Syariah Tbk's stock closing price data. The closing price of shares was analyzed using three average criteria, namely 3, 5, 20, and 100 of the most recent data. Comparison of accuracy with SSE, MSE, and MAPE showed that the best in predicting daily stock closing price data was the Simple Moving Average using the latest 3 data with a prediction result for the future period of Rp. 748, -.