Time series prediction

Ebook Business statistics with Exceland Tableau: A hands-on guide with screencasts and data includes content: Chapter 2: visualization and tableau: telling (true) stories with data, chapter 3: writing up your findings, chapter 4: data and how to get it, chapter 5: testing whether quantities are the same, chapter 6: regression: predicting with continuous variables, chapter 7: checking your regression model, chapter 8: time series introduction and smoothing methods, chapter 9: time series regression methods, chapter 10: optimization, chapter 11: morecomplex optimization, chapter 12: predictin...

247p zizaybay1103 29-05-2024 4 1   Download

This study compares the accuracy of two popular time series forecasting models, namely the autoregressive integrated moving average (ARIMA) model and the long short-term memory (LSTM) model, in predicting daily gold prices from 2000 to 2023. The ARIMA model is a traditional approach that relies on past values to forecast future values, while the LSTM model is a deep learning technique that captures long-term dependencies in time series data.

16p dathienlang1012 03-05-2024 0 0   Download

The primary goal of supply chain management is to meet customer needs through the most efficient use of resources, including the allocation of capacity, inventory and workforce. In theory, the supply chain management seeks to match supply and demand and seeks to do so at minimal cost. And the main indicator of changes in the ratio of supply and demand is the change in the price of products. Trend-seasonal time series models were used to analyze the price behavior in the milk market of the Northern region of Kazakhstan.

8p longtimenosee10 26-04-2024 5 1   Download

Time series data is a series of values observed through repeated measurements at different times. Time series data is a type of data present in almost all different fields of life. Time series prediction is an significant problem in time series data mining.

7p vilarry 01-04-2024 2 1   Download

Runoff prediction has recently become an essential task with respect to assessing the impact of climate change to people’s livelihoods and production. However, the runoff time series always exhibits nonlinear and non-stationary features, which makes it very difficult to be accurately predicted.

12p visystrom 22-11-2023 6 5   Download

Ebook "Data mining with computational intelligence" includes content: Introduction, MLP neural networks for time series prediction and classification, fuzzy neural networks for bioinformatics, an improved RBF neural network classifier, an improved RBF neural network classifier, attribute importance ranking for data dimensionality reduction,...and other contents.

280p haojiubujain07 20-09-2023 5 3   Download

Continued part 1, part 2 of ebook "Practical business statistics" provides readers with contents including: regression and time series; methods and applications; measuring and predicting relationships; predicting one variable from several others; communicating the results of a multiple regression; testing for patterns in qualitative data;...

332p hanlinhchi 29-08-2023 6 5   Download

In this paper, we propose an artificial neural network (ANN) based method to predict the energy use in campus buildings in short-term time series from one hour up to one week. The proposed method analyzes and extracts the features from the historical data of load and temperature to generate the prediction of future energy consumption in the building based on sparsified K-means. To evaluate the performance of the proposed approach, historical load data in hourly resolution collected from the campus buildings in Chonnam National University were used.

13p nhanchienthien 25-07-2023 8 4   Download

The widespread adoption of electronic health records allows us to ask evidence-based questions about the need for and benefits of specific clinical interventions in critical-care settings across large populations.

8p visteverogers 24-06-2023 5 2   Download

In recent years, there has been a surge in interest in the subject of machine learning for prediction. In this study, a temperature dataset of Vietnam’s stations is examined in order to anticipate temperature. Several forecasting models are used to accomplish this goal.

11p vipettigrew 15-03-2023 6 3   Download

The day-to-next day predictions between physical activity (PA) and sleep are not well known, although they are crucial for advancing public health by delivering valid sleep and physical activity recommendations. We used Big Data to examine cross-lagged time-series of sleep and PA over 14 days and nights.

7p viferrari 28-11-2022 12 2   Download

The purpose of this study is to find out the export and import turnover of Vietnam in the future time when Vietnam joins as a part in the transactions of exchanging goods with other countries all over the world. The valuation of transporting products in the future from 2020 to 2023 will be predicted by grey forecasting method with the GM (1,1) model which bases on the historical time series during the period of 2012 to 2019.

8p alucardhellsing 04-05-2022 11 1   Download

Dynamic prediction of patient mortality risk in the ICU with time series data is limited due to high dimensionality, uncertainty in sampling intervals, and other issues. A new deep learning method, temporal convolution network (TCN), makes it possible to deal with complex clinical time series data in ICU.

11p viisaacnewton 26-04-2022 12 1   Download

Predicting dynamics of host-microbial ecosystems is crucial for the rational design of bacteriotherapies. We present MDSINE, a suite of algorithms for inferring dynamical systems models from microbiome time-series data and predicting temporal behaviors. Using simulated data, we demonstrate that MDSINE significantly outperforms the existing inference method.

17p viaristotle 29-01-2022 11 0   Download

We present a novel approach, the Local Edge Machine, for the inference of regulatory interactions directly from time-series gene expression data. We demonstrate its performance, robustness, and scalability on in silico datasets with varying behaviors, sizes, and degrees of complexity. Moreover, we demonstrate its ability to incorporate biological prior information and make informative predictions on a well-characterized in vivo system using data from budding yeast that have been synchronized in the cell cycle.

13p viaristotle 29-01-2022 9 0   Download

Understanding the genetic architecture of temperature adaptation is key for characterizing and predicting the effect of climate change on natural populations. One particularly promising approach is Evolve and Resequence, which combines advantages of experimental evolution such as time series, replicate populations, and controlled environmental conditions, with whole genome sequencing.

25p viarchimedes 26-01-2022 7 0   Download

In this work, a new algorithm is proposed to predict both univariate and multivariate time series based on a combination of clustering, classification and forecasting techniques. The main goal of the proposed algorithm is first to group windows of time series values with similar patterns by applying a clustering process.

17p guernsey 28-12-2021 7 0   Download

In this paper, we propose a novel approach for transforming nancial time-series values into symbolic representation based on value changes. Such approach seems to have few advantages over the existing approaches; one of the most obvious is noise reduction in the data and another one is possibility to ¯nd patterns which are universal for investigating dierent currency pairs.

20p redemption 20-12-2021 22 13   Download

Decreasing availability of water and increasing consumption of it in recent years shows water management plans increase its value. Depending on water consumption in the current and previous years, it is important to predict water consumption in the coming years and make plans accordingly. Evaluation of the performance of water use in agriculture, identifying water resources that are most intensively used and prediction of the potential performance in the coming years have become increasingly important.

8p hanthienngao 30-11-2021 10 1   Download

Saltwater intrusion occurs naturally in a most coastal region; however it could harm a quality of local people. This phenomenon could be perceived with several models. Based on long-time database of salinity concentration collected in Dai estuary (belonging Mekong estuary systems), a number of models were comparated in order to selecting an adequate predictive model for prediction of salinity intrusion in the study area.

4p viwendy2711 05-10-2021 12 1   Download