Parking availability forecasting model

© 2019 IEEE. Parking is increasingly an issue in the world today especially in large and growing cities with contemporary urban mobility. The effort spent in searching for available parking spots results in significant loss of resources such as time, and fuel, as well as environmental pollution. Parking Availability can be influenced by many factors such as time of day, day of week, location, nearby events, weather and traffic conditions. Driven by the idea of predicting parking availability to help drivers plan ahead of time, we contribute a Parking Availability Forecasting Model, which uses a time-series analysis and machine-learning algorithms to predict the number of available parking spots at a certain location on a desired date and time. The forecasting model is trained on historical parking data from the cities of Kansas City, US and Melbourne, Australia. This paper also compares the accuracy of different time-series forecasting models, and how each of them fits our use-case scenario. Multivariate data analysis together with temperature and weather summary are used to cross-validate our forecasting model

Monotonic Neural Ordinary Differential Equation: Time-series Forecasting for Cumulative Data

Time-Series Forecasting based on Cumulative Data (TSFCD) is a crucial problem in decision-making across various industrial scenarios. However, existing time-series forecasting methods often overlook two important characteristics of cumulative data, namely monotonicity and irregularity, which limit their practical applicability. To address this limitation, we propose a principled approach called Monotonic neural Ordinary Differential Equation (MODE) within the framework of neural ordinary differential equations. By leveraging MODE, we are able to effectively capture and represent the monotonicity and irregularity in practical cumulative data. Through extensive experiments conducted in a bonus allocation scenario, we demonstrate that MODE outperforms state-of-the-art methods, showcasing its ability to handle both monotonicity and irregularity in cumulative data and delivering superior forecasting performance.Comment: Accepted as CIKM'23 Applied Research Trac

Propagation of Uncertainty in Bayesian Kernel Models - Application to Multiple-Step Ahead Forecasting

The object of Bayesian modelling is the predictive distribution, which in a forecasting scenario enables improved estimates of forecasted values and their uncertainties. In this paper we focus on reliably estimating the predictive mean and variance of forecasted values using Bayesian kernel based models such as the Gaussian Process and the Relevance Vector Machine. We derive novel analytic expressions for the predictive mean and variance for Gaussian kernel shapes under the assumption of a Gaussian input distribution in the static case, and of a recursive Gaussian predictive density in iterative forecasting. The capability of the method is demonstrated for forecasting of time-series and compared to approximate methods

Comparing deep learning and statistical methods in forecasting crowd distribution from aggregated mobile phone data

Accurately forecasting how crowds of people are distributed in urban areas during daily activities is of key importance for the smart city vision and related applications. In this work we forecast the crowd density and distribution in an urban area by analyzing an aggregated mobile phone dataset. By comparing the forecasting performance of statistical and deep learning methods on the aggregated mobile data we show that each class of methods has its advantages and disadvantages depending on the forecasting scenario. However, for our time-series forecasting problem, deep learning methods are preferable when it comes to simplicity and immediacy of use, since they do not require a time-consuming model selection for each different cell. Deep learning approaches are also appropriate when aiming to reduce the maximum forecasting error. Statistical methods instead show their superiority in providing more precise forecasting results, but they require data domain knowledge and computationally expensive techniques in order to select the best parameters

SISTEM INFORMASI PERAMALAN ANGKA KEJADIAN PENYAKIT DEMAM BERDARAH MENGGUNAKAN MULTIVARIATE FUZZY TIME SERIES

Indonesia merupakan negara dengan iklim tropis yang menyebabkan terjadinya dua musim, penghujan dan kemarau. DB atau Demam Berdarah Dengue merupakan penyakit yang biasanya menyerang pada musim penghujan. Namun tidak menutup kemungkinan Demam Berdarah juga menyerang pada musim kemarau. Kabupaten Demak merupakan salah satu daerah di Provinsi Jawa Tengah dengan angka kejadian Demam Berdarah yang cukup rendah dibandingkan dengan kota dan kabupaten lain. Meskipun begitu, pengendalian Demam Berdarah perlu dilakukan untuk meminimalisir terjadinya lonjakan angka kejadian Demam Berdarah, karena Demam Berdarah merupakan penyakit yang cukup berbahaya. Salah satu bentuk pengendalian angka kejadian DB yang banyak digunakan yaitu menggunakan model peramalan, salah satunya yaitu menggunakan Fuzzy Time Series. Model Multivariate Fuzzy Time Series (MFTS) merupakan pengembangan dari model Fuzzy Time Series yang dapat digunakan untuk melakukan peramalan dengan menggunakan data time series dengan menggunakan lebih dari satu variabel untuk peramalan, dibandingkan dengan metode Fuzzy Time Series biasanya hanya menggunakan satu variabel saja. Data aktual yang digunakan untuk peramalan berupa angka kejadian Demam Berdarah, curah hujan dan hari hujan dari bulan Januari 2013 hingga Desember 2018, dengan skenario peramalan 2 tahun training dan testing, 3 tahun training dan testing, 6 tahun training dan testing. Berdasarkan hasil penelitian yang didapat, model MFTS memiliki nilai MAPE yang rata-rata menghasilkan nilai peramalan yang cukup akurat, dengan nilai MAPE terendah, ada pada skenario 3 tahun pada orde 5 dengan MAPE 10,394%. Kata kunci: Demam Berdarah, Multivariate Fuzzy Time Series, Fuzzy Time Series Indonesia is a country with a tropical climate that causes two seasons, the rainy season and the dry season. DHF or Dengue Hemorrhagic Fever is a disease that usually attacks during the rainy season. But it does not rule out DHF also attacking in the dry season. Demak Regency is one of the regions in Central Java Province with a low incidence of Dengue Fever compared to other cities and districts. Even so, DHF control needs to be done to minimize the occurrence of dengue fever, because DHF is a fairly dangerous disease. One form of controlling the number of DHF events that is widely used is using forecasting models, one of which is using Fuzzy Time Series. The Multivariate Fuzzy Time Series (MFTS) model is a development of the Fuzzy Time Series model that can be used to forecast using time series data by using more than one variable for forecasting, compared to the Fuzzy Time Series method usually using only one variable. The actual data used for forecasting are DHF incidence rates, rainfall and rainy days from January 2013 to December 2018, with a forecast scenario of 2 years of training and testing, 3 years of training and testing, 6 years of training and testing. Based on the research results obtained, the MFTS model has an MAPE value that on average produces a fairly accurate forecasting value, with the lowest MAPE value, there is a scenario of 3 years in order 5 with a MAPE of 10.394%. Keywords: Dengue Fever, Multivariate Fuzzy Time Series, Fuzzy Time Serie