Structural Combination of Seasonal Exponential Smoothing Forecasts Applied to Load Forecasting

This article draws from research on ensembles in computational intelligence to propose structural combinations of forecasts, which are point forecast combinations that are based on information from the parameters of the individual models that generated the forecasts. Two types of structural combination are proposed which use seasonal exponential smoothing as base models, and are applied to forecast short-term electricity demand. Although forecasting performance may depend on how ensembles are generated, results show that the proposed combinations can outperform competitive benchmarks. The methods can be used to forecast other seasonal data and be extended to different types of forecasting models

Forecasting Stock Market Indices Using Gated Recurrent Unit (GRU) Based Ensemble Models: LSTM-GRU

A time sequence analysis is a particular method for looking at a group of data points gathered over a long period of time. Instead of merely randomly or infrequently, time series analyzers gather information from data points over a predetermined length of time at scheduled times. But this kind of research requires more than just accumulating data over time. Data in time series may be analyzed to illustrate how variables change over time, which makes them different from other types of data. To put it another way, time is a crucial element since it demonstrates how the data changes over the period of the information and the outcomes. It offers a predetermined architecture of data dependencies as well as an extra data source. Time Series forecasting is a crucial field in deep learning because many forecasting issues have a temporal component. A time series is a collection of observations that are made sequentially across time. In this study, we examine distinct machine learning, deep learning and ensemble model algorithms to predict Nike stock price. We are going to use the Nike stock price data from January 2006 to January 2018 and make predictions accordingly. The outcome demonstrates that the hybrid LSTM-GRU model outperformed the other models in terms of performance

Penerapan Metode Exponential Smoothing Untuk Peramalan Penjualan Pada Toko Gitar

Toko Gitar adalah toko alat musik terutama menjual gitar, dalam mempersiapkan stok barang, Toko Gitar  masih dilakukan secara manual tanpa memperhitungkan barang laku pada periode sebelumnya yang mengakibatkan kelebihan dan kekurangan stok di gudang. Hal tersebut dapat merugikan dan menghambat keuntungkan bagi perusahaan. Oleh karena itu dibutuhkan suatu peramalan yang diharapkan dapat membantu perusahaan dalam menentukan jumlah stok barang yang akan dipesan. Metode peramalan yang digunakan adalah Metode Exponential Smoothing dengan alpha=0,8 dan standar error Mean Absolute Deviation (MAD). Data yang digunakan diambil dari data stok barang pada tahun 2018 sebagai referensi. Dari hasil uji coba, Standar eror yang diperoleh merupakan jarak antara hasil peramalan, sebagai contoh pada Gitar SQ hasil peramalannya adalah 4.68 dan standar erornya 1.6 yang artinya perjualan pada barang tersebut bisa (4.86 – 1.6)) atau (4.86 + 1.6). hasil peramalan menggunakan Mean Absolute Deviation (MAD) yaitu  meramalkan stok berbagai jenis barang dan didapatkan standar error di atas 50%. Dengan nilai standar error tersebut dapat simpulkan bahwa peramalan ini layak dan dapat diterapkan

Traffic Density Prediction using IoT-based Double Exponential Smoothing

The number of vehicles and currents that tend to increase causes traffic density. A system is proposed to calculate the number of vehicles and predict real-time traffic density. This research uses Haar Cascade to detect the number of cars and motorcycles and the Double Exponential Smoothing (DES) for forecasting the number of vehicles on the road. MAPE describes forecasting accuracy as a base for selecting the best smoothing constant (Alpha). The best test results from June 13 to 20, 2020, are cars on June 14, 2020 (alpha 0.5, MAPE 0%) and Motorcylecycles on June 18, 2020 (alpha 0.5, MAPE 0.1134% ). The most significant MAPE results of the car were on June 15, 2020, with alpha 0.5 and MAPE 2.1073%. The 3 minutes haar cascade detects 72.58% of cars and 81.90% of motorcycles

Multi-step CNN forecasting for COVID-19 multivariate time-series

The new coronavirus (COVID-19) has spread to over 200 countries, with over 36 million confirmed cases as of October 10, 2020. As a result, numerous machine learning models capable of forecasting the epidemic worldwide have been produced. This paper reviews and summarizes the most relevant machine learning forecasting models for COVID-19. The dataset is derived from the world health organization (WHO) COVID-19 dashboard, and it contains official daily counts of COVID-19 cases, fatalities, and vaccination use reported by countries, territories, and regions. We propose various convolutional neural network (CNN) based models such as CNN, single exponential smoothing CNN (S-CNN), moving average CNN (MA-CNN), smoothed moving average CNN (SMA-CNN), and moving average smoothed CNN (MAS-CNN). Here, MAPE and MSE are used to assess the suggested models. MAPE is frequently used to compare accuracy across time series with different scales. MSE, the model must strive for a total forecast equal to the entire demand. That is, optimizing MSE seeks to create a forecast that is right on average and so unbiased. The final result shows that SMA-CNN outperformed its baselines in both MAPE and MSE. The main contribution of this novel forecasting approach is a more accurate result as a base of the strategy of preventing COVID-19 spreads

Short-Term Load Forecasting for Industrial Customers Based on TCN-LightGBM

Accurate and rapid load forecasting for industrial customers has been playing a crucial role in modern power systems. Due to the variability of industrial customers’ activities, individual industrial loads are usually too volatile to forecast accurately. In this paper, a short-term load forecasting model for industrial customers based on the Temporal Convolutional Network (TCN) and Light Gradient Boosting Machine (LightGBM) is proposed. Firstly, a fixed-length sliding time window method is adopted to reconstruct the electrical features. Next, the TCN is utilized to extract the hidden information and long-term temporal relationships in the input features including electrical features, a meteorological feature and date features. Further, a state-of-the-art LightGBM capable of forecasting industrial customers’ loads is adopted. The effectiveness of the proposed model is demonstrated by using datasets from different industries in China, Australia and Ireland. Multiple experiments and comparisons with existing models show that the proposed model provides accurate load forecasting results

Short-Term Load Forecasting for Industrial Customers Based on TCN-LightGBM

Accurate and rapid load forecasting for industrial customers has been playing a crucial role in modern power systems. Due to the variability of industrial customers' activities, individual industrial loads are usually too volatile to forecast accurately. In this paper, a short-term load forecasting model for industrial customers based on the Temporal Convolution Network (TCN) and Light Gradient Boosting Machine (LightGBM) is proposed. Firstly, a fixed-length sliding time window method is adopted to reconstruct the electrical features. Next, the TCN is utilized to extract the hidden information and long-term temporal relationships in the input features including electrical features, a meteorological feature and date features. Further, a state-of-the-art LightGBM capable of forecasting industrial customers' loads is adopted. The effectiveness of the proposed model is demonstrated by using datasets from different industries in China, Australia and Ireland. Multiple experiments and comparisons with existing models show that the proposed model provides accurate load forecasting results

Peramalan Jumlah Kasus Demam Berdarah Di Kabupaten Malang Menggunakan Metode Support Vector Regresi

Demam berdarah dengue (DBD) adalah suatu penyakit yang disebabkan oleh infeksi virus dengue. DBD adalah penyakit akut dengan manifestasi klinis perdarahan yang menimbulkan syok yang berujung kematian. Penyakit Demam Berdarah adalah endemik yang muncul sepanjang tahun, terutama saat musim penghujan ketika nyamuk dalam kondisi optimal untuk berkembang biak, sehingga dapat dibilang persebaran nyamuk demam berdarah bergantung dengan iklim. Pada Tahun 2016, Kabupaten Malang termasuk dalam 3 besar kabupaten dengan jumlah kasus Demam Berdarah Tertinggi di Jawa Timur, oleh karena itu diperlukan peramalan yang akurat agar Dinas Kesehatan Kabupaten Malang dapat mencegah dan mengantisipasi kasus demam berdarah lebih dini. Data hasil peramalan bisa digunakan untuk perencanaan pelayanan medis, seperti penanganan tepat waktu terhadap pasien dan ketersediaan obat-obatan yang dibutuhkan di masa yang akan datang. Tugas akhir ini bertujuan untuk melakukan peramalan kasus demam berdarah di Kabupaten Malang dengan menggunakan metode Suport Vector Regresi (SVR). Dasar pemikiran dari SVR adalah untuk memetakan set data ke ruang fitur dimensi tinggi non-linear dan menyelesaikan permasalahan regresi dalam ruang fitur dimensi ini. Berdasarkan keunggulan dalam kapasitas menangani high dimensional data, SVR menjadi algoritma yang populer dalam memecahkan masalah peramalan. Hasil peramalan yang dilakukan diharapkan memiliki tingkat akurasi yang cukup tinggi dan bisa membantu Dinas Kesehatan Kabupaten Malang dalam menangani kasus demam berdarah di masa mendatang seperti persiapan obat – obatan, pelayanan pasien agar dapat mengurangi korban jiwa yang disebabkan oleh wabah ini. Model Suport Vektor Regresi dapat digunakan untuk meramalkan jumlah kasus demam berdarah dikarenakan memiliki nilai error yang cukup dapat diterima. Hasil peramalan jumlah kasus demam berdarah di beberapa desa Kabupaten Malang pada periode selanjutnya memiliki nilai rata-rata SMAPE : 27.69%, rata-rata MAD : 0.33 dan rata-rata MSE : 0.44. Hasil peramalan ini dapat dibilang cukup baik ================================================================================================ Dengue hemorrhagic fever (DHF) is a disease caused by dengue virus infection. DHF is an acute disease with clinical manifestations of bleeding that cause shock which leads to death. Dengue Fever is an endemic that occurs throughout the year, especially during the rainy season when mosquitoes are in optimal conditions for breeding, so it can be said that the spread of dengue mosquitoes depends on the climate. In 2016, Malang Regency was included in the top 3 districts with the highest number of Dengue Fever cases in East Java, therefore accurate forecasting was needed so that the Malang District Health Office could prevent and anticipate cases of dengue fever earlier. Data forecasting results can be used for planning medical services, such as timely handling of patients and the availability of medicines needed in the future. This final project aims to forecast dengue fever cases in Malang Regency by using the Support Vector Regression (SVR) method. The rationale for SVR is to map data sets to non-linear high-dimensional feature spaces and solve regression problems in this dimensional feature space. Based on excellence in capacity to handle high dimensional data, SVR is a popular algorithm in solving forecasting problems. The forecasting results carried out are expected to have a fairly high level of accuracy and can help the Malang District Health Office in dealing with dengue cases in the future such as preparation of medicines, patient services in order to reduce the fatalities caused by this outbreak. Vector Support Model Regression can be used to predict the number of bleeding deaths because it has a fairly acceptable error value. The results of forecasting the number of dengue fever cases in several villages in Malang Regency in the following period had an average value of SMAPE: 27.69%, the average MAD: 0.33 and the average MSE: 0.44. This forecasting result is considered good enoug

Predicción a corto plazo de Ia demanda horaria de energía eléctrica en España mediante modelos optimizados de Holt-Winters múltiple estacionales

[ES] La desregulación del sector de la electricidad producido por la Ley 54/1997 del Sector Eléctrico provocó un cambio radical en el sistema de gestión de la electricidad, tanto para los productores y distribuidores, como para los propios consumidores. España lideraba un cambio en la política energética buscando una liberalización del mercado en aplicación de la Directiva 96/92/CE del Parlamento Europeo y del Consejo. En este cambio, el Estado abandona la noción de servicio público para el sistema eléctrico y pasa a gestionarse mediante un mercado mayorista operado por sociedades mercantiles. Este cambio se traduce en que la gestión del sistema se realiza mediante un sistema de mercados de oferta y de demanda, y que el Estado únicamente establecerá la regulación pertinente. Dentro del mismo cambio, se organiza el sistema de modo que aparece el transportista único del sistema, Red Eléctrica de España. Originalmente, este ente debe asegurar el suministro y realizar la panificación de la operativa del sistema, pero con la Ley 17/2007 de la adaptación del sector eléctrico se convierte en el transportista único del sistema. La Ley 24/2013, de 26 de diciembre, del Sector Eléctrico además le atribuye aún mayor responsabilidad, siendo el único operador del transporte y del sistema, adquiriendo la necesidad de realizar previsiones de demanda eléctrica que serán utilizadas en el mercado eléctrico, y, además, de precios de la energía. Estas previsiones se realizan habitualmente mediante la utilización de técnicas de series temporales, utilizando modelos de regresión, ARIMA, redes neuronales o de suavizado exponencial. Debido a que la energía eléctrica no es fácilmente acumulable, la producción debe estar ajustada a la demanda. Cualquier desfase entre ambas provoca costes enormes que las empresas del sector eléctrico necesitan evitar. Para ello, demandan predicciones del consumo lo más acertadas y fiables posibles. Esta tesis se centra en el estudio de los modelos de Holt-Winters para ser utilizados en la previsión de demanda eléctrica en España. Estos modelos han demostrado ser sencillos de trabajar y robustos frente a variaciones no controladas y han sido adaptados para trabajar con múltiples estacionalidades. Con ello se han desarrollado nuevos modelos que han permitido mejorar las previsiones. En primer lugar, se estudia la demanda eléctrica en España, como eje fundamental para el desarrollo de la tesis. Se observa cómo la serie dispone de una características muy relevante: una frecuencia de 24 horas, con una media y varianza que no son constantes. Se observa la presencia de varias estacionalidades que se integran en el modelo, así como una enorme influencia de los días festivos y fines de semana. Por último, se detecta una alta volatilidad. Este análisis permite conocer el comportamiento de la serie e introducir los modelos múltiple-estacionales. En segundo lugar, se presentan y analizan los modelos de Holt-Winters múltiple-estacionales, como eje vertebrador de la tesis. Estos modelos son los desarrollados en la tesis para conseguir sus objetivos: se presentan los modelos, se analizan los valores iniciales y la optimización de los parámetros, y finalmente se analizan los parámetros. Finalmente se introducen nuevos elementos en los modelos que permiten mejorar las previsiones realizadas por los mismos. En este aspecto, se incluye la introducción de estacionalidades discretas que permiten modelizar los días festivos; se introducen indicadores turísticos que mejora la previsión en las zonas cuyo producto interior bruto depende altamente del turismo; finalmente, se introduce un modelo híbrido en el que las condiciones climáticas son consideradas y que aumenta la precisión de las previsiones. Por último, esta tesis viene acompañada de un desarrollo de software específico para la explotación del modelo, desarrollado como Toolbox de MATLAB®. En definitiva, se desarr[CA] La desregulació del sector de l'electricitat produït per la Llei 54/1997, del sector elèctric va provocar un canvi radical en el sistema de gestió de l'electricitat, tant per als productors i distribuïdors, com per als propis consumidors. Espanya liderava un canvi en la política energètica buscant una liberalització del mercat aplicant la Directiva 96/92/CE del Parlament Europeu i del Consell. En aquest canvi, l'Estat abandona la noció de servei públic per al sistema elèctric i passa a gestionar-se mitjançant un mercat majorista operat per societats mercantils. Aquest canvi es tradueix en que la gestió del sistema es realitza mitjançant un sistema de mercats d'oferta i de demanda, i que l'Estat únicament ha d'establir la regulació pertinent. Dins el mateix canvi, s'organitza el sistema de manera que apareix el transportista únic del sistema, Red Eléctrica de España. Originalment, aquest ens ha d'assegurar el subministrament i realitzar la panificació de l'operativa del sistema, però amb la Llei 17/2007 de l'adaptació del sector elèctric es converteix en el transportista únic del sistema. La Llei 24/2013, de 26 de desembre, del sector elèctric a més li atribueix a REE ser l'operador únic del transport i del sistema, adquirint encara més gran responsabilitat i la necessitat de realitzar previsions de demanda elèctrica que seran utilitzades en el mercat elèctric, i, a més, de preus de l'energia. Aquestes previsions es fan habitualment mitjançant la utilització de tècniques de sèries temporals, utilitzant models de regressió, ARIMA, xarxes neuronals o de suavitzat exponencial. A causa de que l'energia elèctrica no és fàcilment acumulable, la producció ha d'estar ajustada a la demanda. Qualsevol desfasament entre les dues provoca costos enormes que les empreses del sector elèctric necessiten evitar. Per a això, demanen prediccions del consum el més encertades i fiables possibles. Aquesta tesi se centra en l'estudi dels models de Holt-Winters per ser utilitzats en la previsió de demanda elèctrica a Espanya. Aquests models han demostrat ser senzills de treballar i robustos davant de variacions no controlades i han estat adaptats per treballar amb múltiples estacionalitats. Amb això s'han desenvolupat nous models que han permès millorar les previsions. En primer lloc, s'estudia la demanda elèctrica a Espanya, com a eix fonamental per al desenvolupament de la tesi. S'observa com la sèrie disposa de característiques molt rellevants: una freqüència de 24 hores, amb una mitjana i variància que no són constants. S'observa la presència de diverses estacionalitats que s'integren en el model, així com una enorme influència dels dies festius i caps de setmana. Finalment, es detecta una alta volatilitat. Aquesta anàlisi permet conèixer el comportament de la sèrie i introduir els models múltiple estacionals. En segon lloc, es presenten i s'analitzen els models de Holt-Winters múltiple estacionals, com a eix vertebrador de la tesi. Aquests models són els desenvolupats en la tesi per aconseguir els seus objectius: es presenten els models, s'analitzen els valors inicials i l'optimització dels paràmetres, i finalment s'analitzen els paràmetres. Finalment s'introdueixen nous elements en els models que permeten millorar les previsions realitzades pels mateixos. En aquest aspecte, s'inclou la introducció de estacionalitats discretes que permeten modelitzar els dies festius; s'introdueixen indicadors turístics que millora la previsió en les zones el producte interior brut depèn altament del turisme; finalment, s'introdueix un model híbrid en el qual les condicions climàtiques són considerades i que augmenta la precisió de les previsions. Addicionalment, aquesta tesi ve acompanyada d'un desenvolupament de programari específic per a l'explotació del model, desenvolupat com Toolbox de Matlab®. En definitiva, es desenvolupen i implanten nous models de Holt-Winters que pro[EN] The deregulation of the electricity sector produced by Law 54/1997 of the Electricity Sector caused a radical change in the electricity management system, both for producers and distributors, and for the consumers themselves. Spain was leading a change in energy policy seeking a liberalization of the market by applying Directive 96/92/EC of the European Parliament and the Council. In this change, the State abandons the notion of public service for the electrical system and it is managed through a wholesale market operated by mercantile companies. This change means that the management of the system is carried out through a system of supply and demand markets, and that the State will only establish the relevant regulation. Within the same change, the system is organized so that the single transporter of the system, Red Eléctrica de España, appears. Originally, this entity must ensure the supply and carry out the baking of the operation of the system, but with the law 17/2007 of the adaptation of the electricity sector becomes the only carrier of the system. Law 24/2013, of December 26, of the Electricity Sector also gives it even greater responsibility, acquiring the need to make forecasts of electric demand that will be used in the electricity market, and, in addition, of energy prices. These forecasts are usually made through the use of time series techniques, using regression models, ARIMA, neural networks or exponential smoothing. Because electric power is not easily accumulated, production must be adjusted to the demand. Any gap between the two causes huge costs that companies in the electricity sector need to avoid. For this, they demand predictions of consumption as accurate and reliable as possible. This thesis focuses on the study of Holt-Winters models to be used in forecasting electricity demand in Spain. These models have proven to be simple to work and robust against uncontrolled variations and have been adapted to work with multiple seasons. This new models have been developed that have improved forecasts. In the first place, the electrical demand in Spain is studied, as a fundamental axis for the development of the thesis. It is observed how the series has very relevant characteristics: a frequency of 24 hours, with a mean and variance that are not constant. It is observed the presence of several seasons that are integrated into the model, as well as a huge influence of holidays and weekends. Finally, high volatility is detected. This analysis allows to know the behavior of the series and introduce the multiple seasonal models. Secondly, seasonal multiple Holt-Winters models are presented and analyzed as the backbone of the thesis. These models are those developed in the thesis to achieve their objectives: the models are presented, the initial values and the optimization of the parameters are analyzed, and finally the parameters are analyzed. Finally, new elements are introduced in the models that allow improving the forecasts made by them. In this aspect, the introduction of discrete seasonings that allow modeling holidays is included; Tourist indicators are introduced that improve forecasting in areas whose gross domestic product depends highly on tourism; finally, a hybrid model is introduced in which the climatic conditions are considered and which increases the accuracy of the forecasts. Additionally, this thesis is accompanied by a development of specific software for the exploitation of the model, developed as MATLAB® Toolbox. In short, new models of Holt-Winters are developed and implemented that provide more accurate short-term forecasts, which allow the entities that form the electrical system to better plan and manage the electrical system.Trull Domínguez, Ó. (2020). Predicción a corto plazo de Ia demanda horaria de energía eléctrica en España mediante modelos optimizados de Holt-Winters múltiple estacionales [Tesis doctoral no publicada]. Universitat Politècnica de València. https://doi.org/10.4995/Thesis/10251/140091TESI