scholarly journals Machine Learning and Statistical Methods for Time Series Forecasting: a Case Study with Water Demand

2021 ◽  
Author(s):  
Mohamed Zerara
Keyword(s):  
Machine Learning ◽  
Time Series ◽  
Statistical Methods ◽  
Water Demand ◽  
Potable Water ◽  
Time Series Forecasting ◽  
Public Company ◽  
Statistical Approaches

<div>In this study, machine learning and statistical approaches are used to forecast water demand of a public company which is in charge of distributing potable water. Different methods and algorithms are briefly introduced, then assessed with the water demand case study and discussed in details.<br></div>

scholarly journals Machine Learning and Statistical Methods for Time Series Forecasting: a Case Study with Water Demand

2021 ◽  
Author(s):  
Mohamed Zerara
Keyword(s):  
Machine Learning ◽  
Time Series ◽  
Statistical Methods ◽  
Water Demand ◽  
Potable Water ◽  
Time Series Forecasting ◽  
Public Company ◽  
Statistical Approaches

<div>In this study, machine learning and statistical approaches are used to forecast water demand of a public company which is in charge of distributing potable water. Different methods and algorithms are briefly introduced, then assessed with the water demand case study and discussed in details.<br></div>

scholarly journals Machine Learning and Statistical Methods for Time Series Forecasting: a Case Study with Water Demand

2021 ◽  
Author(s):  
Mohamed Zerara
Keyword(s):  
Machine Learning ◽  
Time Series ◽  
Statistical Methods ◽  
Water Demand ◽  
Potable Water ◽  
Time Series Forecasting ◽  
Public Company ◽  
Statistical Approaches

<div>In this study, machine learning and statistical approaches are used to forecast water demand of a public company which is in charge of distributing potable water. Different methods and algorithms are briefly introduced, then assessed with the water demand case study and discussed in details.<br></div>

Deep Learning for text in limted data settings

2020 ◽  
Author(s):  
Pathikkumar Patel ◽  
Bhargav Lad ◽  
Jinan Fiaidhi
Keyword(s):  
Machine Learning ◽  
Time Series ◽  
Deep Learning ◽  
Sentiment Analysis ◽  
Transfer Learning ◽  
Text Classification ◽  
State Of The Art ◽  
Time Series Forecasting ◽  
Text Data ◽  
Performance Levels

During the last few years, RNN models have been extensively used and they have proven to be better for sequence and text data. RNNs have achieved state-of-the-art performance levels in several applications such as text classification, sequence to sequence modelling and time series forecasting. In this article we will review different Machine Learning and Deep Learning based approaches for text data and look at the results obtained from these methods. This work also explores the use of transfer learning in NLP and how it affects the performance of models on a specific application of sentiment analysis.

scholarly journals Prediksi Kunjungan Wisatawan Taman Nasional Gunung Merbabu dengan Time Series Forecasting dan LSTM

2020 ◽  
Vol 11 (2) ◽  
pp. 131
Author(s):  
Josua Manullang ◽  
Albertus Joko Santoso ◽  
Andi Wahju Rahardjo Emanuel
Keyword(s):  
Time Series ◽  
National Park ◽  
Short Term Memory ◽  
Lag Time ◽  
Time Series Forecasting ◽  
Mean Square ◽  
Short Term ◽  
Term Memory ◽  
Long Short Term Memory

Abstract. Prediction of tourist visits of Mount Merbabu National Park (TNGMb) needs to be done to control the number of visitors and to preserve the national park. The combination of time series forecasting (TSF) and deep learning methods has become a new alternative for prediction. This case study was conducted to implement several methods combination of TSF and Long-Short Term Memory (LSTM) to predict the visits. In this case study, there are 18 modelling scenarios as research objects to determine the best model by utilizing tourist visits data from 2013 to 2018. The results show that the model applying the lag time method can improve the model's ability to capture patterns on time series data. The error value is measured using the root mean square error (RMSE), with the smallest value of 3.7 in the LSTM architecture, using seven lags as a feature and one lag as a label.Keywords: Tourist Visit, Taman Nasional Gunung Merbabu, Prediction, Recurrent Neural Network, Long-Short Term MemoryAbstrak. Prediksi kunjungan wisatawan Taman Nasional Gunung Merbabu (TNGMb) perlu dilakukan untul pengendalian jumlah pengunjung dan menjaga kelestarian taman nasional. Gabungan metode antara time series forecasting (TSF) dan deep learning telah menjadi alternatif baru untuk melakukan prediksi. Studi kasus ini dilakukan untuk mengimplementasi gabungan dari beberapa macam metode antara TSF dan Long-Short Term Memory (LSTM) untuk memprediksi kunjungan pada TNGMb. Pada studi kasus ini, terdapat 18 skenario pemodelan sebagai objek penelitian untuk menentukan model terbaik, dengan memanfaatkan data jumlah kunjungan wisatawan di TNGMb mulai dari tahun 2013 sampai dengan tahun 2018. Hasil prediksi menunjukkan pemodelan dengan menerapkan metode lag time dapat meningkatakan kemampuan model untuk menangkap pola pada data deret waktu. Besar nilai kesalahan diukur menggunakan root mean square error (RMSE), dengan nilai terkecil sebesar 3,7 pada arsitektur LSTM, menggunakan tujuh lag sebagai feature dan satu lag sebagai label. Kata Kunci: Kunjungan Wisatawan, Taman Nasional Gunung Merbabu, Prediksi, Recurrent Neural Network, Long-Short Term Memory

An intelligent hybridization of ARIMA with machine learning models for time series forecasting

2019 ◽  
Vol 175 ◽  
pp. 72-86 ◽  
Author(s):  
Domingos S. de O. Santos Júnior ◽  
João F.L. de Oliveira ◽  
Paulo S.G. de Mattos Neto
Keyword(s):  
Machine Learning ◽  
Time Series ◽  
Time Series Forecasting ◽  
Learning Models ◽  
Machine Learning Models

Time Series Forecasting in Retail Sales Using LSTM and Prophet

2021 ◽  
pp. 241-262
Author(s):  
Clony Junior ◽  
Pedro Gusmão ◽  
José Moreira ◽  
Ana Maria M. Tome
Keyword(s):  
Time Series ◽  
Data Science ◽  
Short Term Memory ◽  
Special Kind ◽  
Time Series Forecasting ◽  
Short Term ◽  
Term Memory ◽  
Use Of Time

Data science highlights fields of study and research such as time series, which, although widely explored in the past, gain new perspectives in the context of this discipline. This chapter presents two approaches to time series forecasting, long short-term memory (LSTM), a special kind of recurrent neural network (RNN), and Prophet, an open-source library developed by Facebook for time series forecasting. With a focus on developing forecasting processes by data mining or machine learning experts, LSTM uses gating mechanisms to deal with long-term dependencies, reducing the short-term memory effect inherent to the traditional RNN. On the other hand, Prophet encapsulates statistical and computational complexity to allow broad use of time series forecasting, prioritizing the expert's business knowledge through exploration and experimentation. Both approaches were applied to a retail time series. This case study comprises daily and half-hourly forecasts, and the performance of both methods was measured using the standard metrics.

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