scholarly journals Correlation Determination between COVID-19 and Weather Parameters Using Time Series Forecasting: A Case Study in Pakistan

2021 ◽  
Vol 2021 ◽  
pp. 1-9
Author(s):  
Humera Batool ◽  
Lixin Tian
Keyword(s):  
Time Series ◽  
Prediction Models ◽  
Mean Squared Error ◽  
Time Series Prediction ◽  
Detailed Examination ◽  
Percentage Error ◽  
Mortality Data ◽  
Squared Error ◽  
Temperature And Humidity ◽  
Weather Parameters

Infectious diseases like COVID-19 spread rapidly and have led to substantial economic loss worldwide, including in Pakistan. The effect of weather on COVID-19 spreading needs more detailed examination, as some studies have claimed to mitigate its spread. COVID-19 was declared a pandemic by WHO and has been reported in about 210 countries worldwide, including Asia, Europe, the USA, and North America. Person-to-person contact and international air travel between the nations were the leading causes behind the spreading of SARS-CoV-2 from its point of origin, besides the natural forces. However, further spread and infection within the community or country can be aided by natural elements, such as the weather. Therefore, the correlation between COVID-19 and temperature can be better elucidated in countries like Pakistan, where SARS-CoV-2 has affected at least 0.37 million people. This study collected Pakistan’s COVID-19 infection and mortality data for ten months (March–December 2020). Related weather parameters, temperature, and humidity were also obtained for the same course of time. The collected data were processed and used to compare the performance of various time series prediction models in terms of mean squared error (MSE), root-mean-squared error (RMSE), and mean absolute percentage error (MAPE). This paper, using the time series model, estimates the effect of humidity, temperature, and other weather parameters on COVID-19 transmission by obtaining the correlation among the total infected cases and the number of deaths and weather variables in a particular region. Results depict that weather parameters hold more influence in evaluating the sum number of cases and deaths than other factors like community, age, and the total population. Therefore, temperature and humidity are salient parameters for predicting COVID-19 affected instances. Moreover, it is concluded that the higher the temperature, the lesser the mortality due to COVID-19 infection.

scholarly journals Optimization of IoT-Enabled Physical Location Monitoring Using DT and VAR

2021 ◽  
Vol 15 (4) ◽  
pp. 1-28
Author(s):  
Ajitkumar Sureshrao Shitole ◽  
Manoj Himmatrao Devare
Keyword(s):  
Time Series ◽  
Mean Squared Error ◽  
Time Series Prediction ◽  
Multivariate Time Series ◽  
Local System ◽  
Sensor Data ◽  
Physical Location ◽  
Squared Error ◽  
And Storage ◽  
Auto Regression

This study shows an enhancement of IoT which gets sensor data and performs real-time face recognition to screen physical areas to find strange situations and send an alarm mail to the client to make remedial moves to avoid any potential misfortune in the environment. Sensor data is pushed onto the local system and GoDaddy Cloud, whenever the camera detects a person to optimize the Physical Location Monitoring System by reducing the bandwidth requirement and storage cost onto the Cloud using edge computation. The study reveals that Decision Tree (DT) and Random Forest give reasonably similar macro average f1-score to predict a person using sensor data. Experimental results show that DT is the most reliable predictive model for the Cloud datasets of three different physical locations to predict a person using timestamp with an accuracy of 83.99%, 88.92%, and 80.97%. This study also explains multivariate time series prediction using Vector Auto Regression that gives reasonably good Root Mean Squared Error to predict Temperature, Humidity, Light Dependent Resistor, and Gas time series.

scholarly journals PREDIKSI DATA TRANSAKSI PENJUALAN TIME SERIES MENGGUNAKAN REGRESI LSTM

2020 ◽  
Vol 9 (1) ◽  
pp. 1
Author(s):  
Marie Luthfi Ashari ◽  
Mujiono Sadikin
Keyword(s):  
Machine Learning ◽  
Time Series ◽  
Short Term Memory ◽  
Mean Squared Error ◽  
Percentage Error ◽  
Short Term ◽  
Term Memory ◽  
Root Mean Squared Error ◽  
Squared Error ◽  
Long Short Term Memory

Sebagai upaya untuk memenangkan persaingan di pasar, perusahaan farmasi harus menghasilkan produk obat – obatan yang berkualitas. Untuk menghasilkan produk yang berkualitas, diperlukan perencanaan produksi yang baik dan efisien. Salah satu dasar perencanaan produksi adalah prediksi penjualan. PT. Metiska Farma telah menerapkan metode prediksi dalam proses produksi, akan tetapi prediksi yang dihasilkan tidak akurat sehingga menyebabkan tidak optimal dalam memenuhi permintaan pasar. Untuk meminimalisir masalah kurang akuratnya proses prediksi tersebut, dalam penelitian yang disajikan pada makalah ini dilakukan uji coba prediksi menggunakan teknik Machine Learning dengan metode Regresi Long Short Term Memory (LSTM). Teknik yang diusulkan diuji coba menggunakan dataset penjualan produk “X” dari PT. Metiska Farma dengan parameter kinerja Root Mean Squared Error (RMSE) dan MAPE (Mean Absolute Percentage Error). Hasil penelitian ini berupa nilai rata – rata evaluasi error dari pemodelan data training dan data testing. Di mana hasil menunjukan bahwa Regresi LSTM memiliki nilai prediksi penjualan dengan evaluasi model melalui RMSE sebesar 286.465.424 untuk data training dan 187.013.430 untuk data testing. Untuk nilai MAPE sebesar 787% dan 309% untuk data training dan data testing secara berurut.

scholarly journals A Nonlinear Optimizated PSO-SVR Hybrid System for Time Series Forecasting with ARIMA

2021 ◽  
Author(s):  
Emilly Pereira Alves ◽  
Joao Fausto Lorenzato Oliveira ◽  
Manoel Henrique da Nóbrega Marinho ◽  
Francisco Madeiro
Keyword(s):  
Time Series ◽  
Mean Squared Error ◽  
Moving Average ◽  
Absolute Error ◽  
Percentage Error ◽  
Support Vector ◽  
Autoregressive Integrated Moving Average ◽  
Linear Pattern ◽  
Squared Error ◽  
The Subject

In the forecasting time series field, the combination of techniques to aid in predicting different patterns has been the subject of several studies. Hybrid models have been widely applied in this scenario, where the vast majority of series are composed of linear and nonlinear patterns. The Autoregressive Integrated Moving Average (ARIMA) presents satisfactory results in a linear pattern prediction but can not capture nonlinear ones. In dealing with nonlinear patterns, the Support Vector Regression (SVR) has shown promising results. In order to map both patterns, an optimized nonlinear combination model based on SVR and ARIMA is proposed. The main difference in comparison with other works is the use of an interactive Particle Swarm Optimization (PSO) to increase the prediction performance. To the experimental setup, six well-known datasets of the literature is used. The performance is assessed by the metrics Mean Squared Error (MSE), Mean Absolute Percentage Error (MAPE), and Mean Absolute Error (MAE). The results show the proposed system attains better outcomes when compared to the other tested techniques, for most of the used data.

Workload Prediction on Google Cluster Trace

2014 ◽  
Vol 6 (3) ◽  
pp. 34-52 ◽  
Author(s):  
Md. Rasheduzzaman ◽  
Md. Amirul Islam ◽  
Rashedur M. Rahman
Keyword(s):  
Resource Utilization ◽  
Prediction Models ◽  
Mean Squared Error ◽  
Experimental Result ◽  
Percentage Error ◽  
Support Vector ◽  
Workload Prediction ◽  
Inference System ◽  
Squared Error ◽  
Efficient Resource

Workload prediction in cloud systems is an important task to ensure maximum resource utilization. So, a cloud system requires efficient resource allocation to minimize the resource cost while maximizing the profit. One optimal strategy for efficient resource utilization is to timely allocate resources according to the need of applications. The important precondition of this strategy is obtaining future workload information in advance. The main focus of this analysis is to design and compare different forecasting models to predict future workload. This paper develops model through Adaptive Neuro Fuzzy Inference System (ANFIS), Non-linear Autoregressive Network with Exogenous inputs (NARX), Autoregressive Integrated Moving Average (ARIMA), and Support Vector Regression (SVR). Public trace data (workload trace version II) which is made available by Google were used to verify the accuracy, stability and adaptability of different models. Finally, this paper compares these prediction models to find out the model which ensures better prediction. Performance of forecasting techniques is measured by some popular statistical metric, i.e., Root Mean Squared Error (RMSE), Mean Absolute Error (MAE), Mean Absolute Percentage Error (MAPE), Sum of Squared Error (SSE), Normalized Mean Squared Error (NMSE). The experimental result indicates that NARX model outperforms other models, e.g., ANFIS, ARIMA, and SVR.

scholarly journals Preliminaries on the Accurate Estimation of the Hurst Exponent Using Time Series

2021 ◽  
Author(s):  
Ginno Millán ◽  
Román Osorio-Comparán ◽  
Gastón Lefranc
Keyword(s):  
Time Series ◽  
Hurst Exponent ◽  
High Speed ◽  
Mean Squared Error ◽  
Computer Network ◽  
Accurate Estimation ◽  
Minimum Length ◽  
Squared Error ◽  
In Series ◽  
Speed Computer

<div>This article explores the required amount of time series points from a high-speed computer network to accurately estimate the Hurst exponent. The methodology consists in designing an experiment using estimators that are applied to time series addresses resulting from the capture of high-speed network traffic, followed by addressing the minimum amount of point required to obtain in accurate estimates of the Hurst exponent. The methodology addresses the exhaustive analysis of the Hurst exponent considering bias behaviour, standard deviation, and Mean Squared Error using fractional Gaussian noise signals with stationary increases. Our results show that the Whittle estimator successfully estimates the Hurst exponent in series with few</div><div>points. Based on the results obtained, a minimum length for the time series is empirically proposed. Finally, to validate the results, the methodology is applied to real traffic captures in a high-speed computer network.</div>

scholarly journals Peramalan Jumlah Populasi Sapi Potong di Kalimantan Selatan Menggunakan Metode Moving Average, Exponential Smoothing dan Trend Analysis

2019 ◽  
Vol 6 (1) ◽  
pp. 41
Author(s):  
Jaka Darma Jaya
Keyword(s):  
Trend Analysis ◽  
Mean Squared Error ◽  
Moving Average ◽  
Exponential Smoothing ◽  
Mean Absolute Percentage Error ◽  
Percentage Error ◽  
Absolute Percentage Error ◽  
Polynomial Trend ◽  
Squared Error

Perkembangan produksi daging sapi di Indonesia selama 30 tahun terakhir secara umum cenderung meningkat. Kebutuhan daging sapi di Indonesia masih belum bisa dicukupi oleh supply domestik, sehingga diperlukan impor daging sapi dari luar negeri.  Diperlukan kajian tentang proyeksi ketersediaan populasi sapi potong di masa mendatang agar diambil kebijakan yang tepat dalam menjaga stabilitas dan keterpenuhan supply daging nasional.  Penelitian ini bertujuan untuk melakukan peramalan jumlah populasi sapi potong menggunakan 3 (tiga) metode peramalan yaitu metode moving average, exponential smoothing dan trend analysis.  Hasil peramalan ini selanjutnya diukur akurasinya menggunakan MAD (Mean Absolud Deviation), MSE (Mean Squared Error) dan MAPE (Mean Absolute Percentage Error).  Proyeksi populasi sapi potong pada tahun 2019 (periode berikutnya) menggunakan 3 metode peramalan adalah: 195.100 (moving average); 218.225 (exponential smooting) dan 262.899 (trend analysis). Pengukuran akurasi menggunakan MAD, MSE dan MAPE menunjukkan bahwa metode peramalan jumlah populasi sapi potong yang paling akurat adalah peramalan menggunakan metode polynomial trend analysis (MAD 14.716,12;  MSE 327.282.084,17; dan MAPE 0,09) karena memiliki tingkat kesalahan yang lebih kecil dibandingkan hasil peramalan menggunakan metode moving average dan exponential smoothing.

scholarly journals A Period-Aware Hybrid Model Applied for Forecasting AQI Time Series

2020 ◽  
Vol 12 (11) ◽  
pp. 4730 ◽  
Author(s):  
Ping Wang ◽  
Hongyinping Feng ◽  
Guisheng Zhang ◽  
Daizong Yu
Keyword(s):  
Time Series ◽  
Air Quality ◽  
Hybrid Model ◽  
Prediction Models ◽  
Time Series Prediction ◽  
Input Vector ◽  
Period Function ◽  
Data Set ◽  
Luenberger Observer ◽  
Extraction Algorithm

An accurate, reliable and stable air quality prediction system is conducive to the public health and management of atmospheric ecological environment; therefore, many models, individual or hybrid, have been implemented widely to deal with the prediction problem. However, many of these models do not take into consideration or extract improperly the period information in air quality index (AQI) time series, which impacts the models’ learning efficiency greatly. In this paper, a period extraction algorithm is proposed by using a Luenberger observer, and then a novel period-aware hybrid model combined the period extraction algorithm and tradition time series models is build to exploit the comprehensive forecasting capacity to the AQI time series with nonlinear and non-stationary noise. The hybrid model requires a multi-phase implementation. In the first step, the Luenberger observer is used to estimate the implied period function in the one-dimensional AQI series, and then the analyzed time series is mapped to the period space through the function to obtain the period information sub-series of the original series. In the second step, the period sub-series is combined with the original input vector as input vector components according to the time points to establish a new data set. Finally, the new data set containing period information is applied to train the traditional time series prediction models. Both theoretical proof and experimental results obtained on the AQI hour values of Beijing, Tianjin, Taiyuan and Shijiazhuang in North China prove that the hybrid model with period information presents stronger robustness and better forecasting accuracy than the traditional benchmark models.

scholarly journals Spatiotemporal Approaches for Quality Control and Error Correction of Atmospheric Data through Machine Learning

2020 ◽  
Vol 2020 ◽  
pp. 1-12 ◽  
Author(s):  
Hye-Jin Kim ◽  
Sung Min Park ◽  
Byung Jin Choi ◽  
Seung-Hyun Moon ◽  
Yong-Hyuk Kim
Keyword(s):  
Machine Learning ◽  
Time Series ◽  
Quality Control ◽  
Mean Squared Error ◽  
Machine Learning Algorithms ◽  
Support Vector ◽  
Weather Element ◽  
Applied Machine Learning ◽  
Squared Error ◽  
Atmospheric Data

We propose three quality control (QC) techniques using machine learning that depend on the type of input data used for training. These include QC based on time series of a single weather element, QC based on time series in conjunction with other weather elements, and QC using spatiotemporal characteristics. We performed machine learning-based QC on each weather element of atmospheric data, such as temperature, acquired from seven types of IoT sensors and applied machine learning algorithms, such as support vector regression, on data with errors to make meaningful estimates from them. By using the root mean squared error (RMSE), we evaluated the performance of the proposed techniques. As a result, the QC done in conjunction with other weather elements had 0.14% lower RMSE on average than QC conducted with only a single weather element. In the case of QC with spatiotemporal characteristic considerations, the QC done via training with AWS data showed performance with 17% lower RMSE than QC done with only raw data.

scholarly journals Novel FTLRNN with Gamma Memory for Short-Term and Long-Term Predictions of Chaotic Time Series

2009 ◽  
Vol 2009 ◽  
pp. 1-21
Author(s):  
Sanjay L. Badjate ◽  
Sanjay V. Dudul
Keyword(s):  
Neural Network ◽  
Time Series ◽  
Mean Squared Error ◽  
Chaotic Time Series ◽  
Nonstationary Time Series ◽  
Medium Term ◽  
Short Term ◽  
Squared Error ◽  
Wide Range

Multistep ahead prediction of a chaotic time series is a difficult task that has attracted increasing interest in the recent years. The interest in this work is the development of nonlinear neural network models for the purpose of building multistep chaotic time series prediction. In the literature there is a wide range of different approaches but their success depends on the predicting performance of the individual methods. Also the most popular neural models are based on the statistical and traditional feed forward neural networks. But it is seen that this kind of neural model may present some disadvantages when long-term prediction is required. In this paper focused time-lagged recurrent neural network (FTLRNN) model with gamma memory is developed for different prediction horizons. It is observed that this predictor performs remarkably well for short-term predictions as well as medium-term predictions. For coupled partial differential equations generated chaotic time series such as Mackey Glass and Duffing, FTLRNN-based predictor performs consistently well for different depths of predictions ranging from short term to long term, with only slight deterioration after k is increased beyond 50. For real-world highly complex and nonstationary time series like Sunspots and Laser, though the proposed predictor does perform reasonably for short term and medium-term predictions, its prediction ability drops for long term ahead prediction. However, still this is the best possible prediction results considering the facts that these are nonstationary time series. As a matter of fact, no other NN configuration can match the performance of FTLRNN model. The authors experimented the performance of this FTLRNN model on predicting the dynamic behavior of typical Chaotic Mackey-Glass time series, Duffing time series, and two real-time chaotic time series such as monthly sunspots and laser. Static multi layer perceptron (MLP) model is also attempted and compared against the proposed model on the performance measures like mean squared error (MSE), Normalized mean squared error (NMSE), and Correlation Coefficient (r). The standard back-propagation algorithm with momentum term has been used for both the models.

scholarly journals Optimal derotation of shared acceleration time series by determining relative spatial alignment

2015 ◽  
Vol 11 (4) ◽  
pp. 454-466 ◽  
Author(s):  
René Mayrhofer ◽  
Helmut Hlavacs ◽  
Rainhard Dieter Findling
Keyword(s):  
Time Series ◽  
Mean Squared Error ◽  
Three Dimensions ◽  
Content Type ◽  
Acceleration Time ◽  
Squared Error ◽  
Spatial Alignment ◽  
Optimal Rotation ◽  
Multiple Devices ◽  
Device Pairing

Purpose – The purpose of this article is to improve detection of common movement. Detecting if two or multiple devices are moved together is an interesting problem for different applications. However, these devices may be aligned arbitrarily with regards to each other, and the three dimensions sampled by their respective local accelerometers can therefore not be directly compared. The typical approach is to ignore all angular components and only compare overall acceleration magnitudes – with the obvious disadvantage of discarding potentially useful information. Design/methodology/approach – This paper contributes a method to analytically determine relative spatial alignment of two devices based on their acceleration time series. The method uses quaternions to compute the optimal rotation with regards to minimizing the mean squared error. Findings – Based on real-world experimental data from smartphones and smartwatches shaken together, the paper demonstrates the effectiveness of the method with a magnitude squared coherence metric, for which an improved equal error rate (EER) of 0.16 (when using derotation) over an EER of 0.18 (when not using derotation) is shown. Practical implications – After derotation, the reference system of one device can be (locally and independently) aligned with the other, and thus all three dimensions can consequently be compared for more accurate classification. Originality/value – Without derotating time series, angular information cannot be used for deciding if devices have been moved together. To the best of the authors ' knowledge, this is the first analytic approach to find the optimal derotation of the coordinate systems, given only the two 3D time acceleration series of devices (supposedly) moved together. It can be used as the basis for further research on improved classification toward acceleration-based device pairing.

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