scholarly journals An Efficient Prediction Model for Water Discharge in Schoharie Creek, NY

2014 ◽  
Vol 2014 ◽  
pp. 1-10 ◽  
Author(s):  
Katerina G. Tsakiri ◽  
Antonios E. Marsellos ◽  
Igor G. Zurbenko
Keyword(s):  
New York ◽  
Time Series ◽  
Water Discharge ◽  
Winter Period ◽  
Statistical Methodology ◽  
Summer Period ◽  
Short Term ◽  
Efficient Prediction ◽  
Climatic Time Series

Flooding normally occurs during periods of excessive precipitation or thawing in the winter period (ice jam). Flooding is typically accompanied by an increase in river discharge. This paper presents a statistical model for the prediction and explanation of the water discharge time series using an example from the Schoharie Creek, New York (one of the principal tributaries of the Mohawk River). It is developed with a view to wider application in similar water basins. In this study a statistical methodology for the decomposition of the time series is used. The Kolmogorov-Zurbenko filter is used for the decomposition of the hydrological and climatic time series into the seasonal and the long and the short term component. We analyze the time series of the water discharge by using a summer and a winter model. The explanation of the water discharge has been improved up to 81%. The results show that as water discharge increases in the long term then the water table replenishes, and in the seasonal term it depletes. In the short term, the groundwater drops during the winter period, and it rises during the summer period. This methodology can be applied for the prediction of the water discharge at multiple sites.

scholarly journals Long-Term Data Traffic Forecasting for Network Dimensioning in LTE with Short Time Series

Electronics ◽  
2021 ◽  
Vol 10 (10) ◽  
pp. 1151
Author(s):  
Carolina Gijón ◽  
Matías Toril ◽  
Salvador Luna-Ramírez ◽  
María Luisa Marí-Altozano ◽  
José María Ruiz-Avilés
Keyword(s):  
Time Series ◽  
Deep Learning ◽  
Time Series Analysis ◽  
Data Traffic ◽  
Medium Term ◽  
Short Term ◽  
Traffic Forecasting ◽  
Series Analysis ◽  
Radio Planning

Network dimensioning is a critical task in current mobile networks, as any failure in this process leads to degraded user experience or unnecessary upgrades of network resources. For this purpose, radio planning tools often predict monthly busy-hour data traffic to detect capacity bottlenecks in advance. Supervised Learning (SL) arises as a promising solution to improve predictions obtained with legacy approaches. Previous works have shown that deep learning outperforms classical time series analysis when predicting data traffic in cellular networks in the short term (seconds/minutes) and medium term (hours/days) from long historical data series. However, long-term forecasting (several months horizon) performed in radio planning tools relies on short and noisy time series, thus requiring a separate analysis. In this work, we present the first study comparing SL and time series analysis approaches to predict monthly busy-hour data traffic on a cell basis in a live LTE network. To this end, an extensive dataset is collected, comprising data traffic per cell for a whole country during 30 months. The considered methods include Random Forest, different Neural Networks, Support Vector Regression, Seasonal Auto Regressive Integrated Moving Average and Additive Holt–Winters. Results show that SL models outperform time series approaches, while reducing data storage capacity requirements. More importantly, unlike in short-term and medium-term traffic forecasting, non-deep SL approaches are competitive with deep learning while being more computationally efficient.

scholarly journals Track Irregularity Time Series Analysis and Trend Forecasting

2012 ◽  
Vol 2012 ◽  
pp. 1-15 ◽  
Author(s):  
Jia Chaolong ◽  
Xu Weixiang ◽  
Wang Futian ◽  
Wang Hanning
Keyword(s):  
Time Series ◽  
Time Series Data ◽  
Series Data ◽  
Short Term ◽  
Measuring Point ◽  
Trend Forecasting ◽  
Changing Trend ◽  
Track Irregularity ◽  
Linear Differential

The combination of linear and nonlinear methods is widely used in the prediction of time series data. This paper analyzes track irregularity time series data by using gray incidence degree models and methods of data transformation, trying to find the connotative relationship between the time series data. In this paper, GM(1,1)is based on first-order, single variable linear differential equations; after an adaptive improvement and error correction, it is used to predict the long-term changing trend of track irregularity at a fixed measuring point; the stochastic linear AR, Kalman filtering model, and artificial neural network model are applied to predict the short-term changing trend of track irregularity at unit section. Both long-term and short-term changes prove that the model is effective and can achieve the expected accuracy.

scholarly journals Long Short Term Memory Recurrent Network for Standard and Poor’s 500 Index Modelling

2018 ◽  
Vol 7 (4.15) ◽  
pp. 25 ◽  
Author(s):  
Said Jadid Abdulkadir ◽  
Hitham Alhussian ◽  
Muhammad Nazmi ◽  
Asim A Elsheikh
Keyword(s):  
Neural Network ◽  
Time Series ◽  
Recurrent Neural Network ◽  
Time Series Data ◽  
Short Term Memory ◽  
Series Data ◽  
Short Term ◽  
Term Memory ◽  
Long Short Term Memory

Forecasting time-series data are imperative especially when planning is required through modelling using uncertain knowledge of future events. Recurrent neural network models have been applied in the industry and outperform standard artificial neural networks in forecasting, but fail in long term time-series forecasting due to the vanishing gradient problem. This study offers a robust solution that can be implemented for long-term forecasting using a special architecture of recurrent neural network known as Long Short Term Memory (LSTM) model to overcome the vanishing gradient problem. LSTM is specially designed to avoid the long-term dependency problem as their default behavior. Empirical analysis is performed using quantitative forecasting metrics and comparative model performance on the forecasted outputs. An evaluation analysis is performed to validate that the LSTM model provides better forecasted outputs on Standard & Poor’s 500 Index (S&P 500) in terms of error metrics as compared to other forecasting 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.

29 SHORT-TERM v. LONG-TERM PROGESTERONE PROTOCOL USING CERVICAL OR INTRAUTERINE FIXED-TIME INSEMINATION IN SHEEP

2010 ◽  
Vol 22 (1) ◽  
pp. 172
Author(s):  
A. Menchaca ◽  
M. Vilariño ◽  
E. Rubianes
Keyword(s):  
New York ◽  
Pregnancy Rate ◽  
Body Condition Score ◽  
Skim Milk ◽  
Prostaglandin F2α ◽  
Fixed Time ◽  
Short Term ◽  
Condition Score ◽  
N 93

The short-term protocol with progesterone, prostaglandin F2α (PGF2α), and eCG is used to control follicular dynamics and luteal activity synchronizing the ovulation for fixed-time AI in sheep. The objective of this experiment was to compare the pregnancy rate obtained with short-term protocol (6 d) and long-term protocol (14 d) using cervical or intrauterine fixed-time AI in sheep. Three hundred fifty-two Merino ewes with a body condition score of 2.9 ± 0.3 (mean ± SD; scale 0 to 5) were used during the breeding season (April, 33S, Uruguay). All the females received a CIDR-G (0.3 g of progesterone, InterAg, Hamilton, New Zealand) for 6 d (short-term protocol; n = 178) or 14 d (long-term protocol, n = 174). One imdose of eCG (300 IU, Novormon, Syntex, BA, Argentina) was given at the moment of device withdrawal for the both protocols, and one imdose of PGF2α (10 mg of dinoprost, Lutalyse, Pfizer, New York, NY, USA) was given at the end of the short-term protocol to ensure luteolysis. Cervical AI (short-term protocol, n = 85; long-term protocol, n = 104) or intrauterine AI (short-term protocol, n = 93; long-term protocol, n = 70) was performed 48 or 54 h after device withdrawal, using 200 × 106 or 100 × 106 spermatozoa per ewe, respectively. Fresh semen was extended in UHT skim milk (1000 × 106 spermatozoa mL-1) and used within 1 h of collection. Estrus was recorded twice a day for 4 days after device withdrawal using vasectomized males. Pregnancy diagnosis was performed by transrectal ultrasonography 40 d after AI (5.0 MHz, Aloka, Tokyo, Japan). Logistic regression was used to analyze the effect of the treatment (P < 0.05), the AI technique (P < 0.05), and their interaction (P = NS). Pregnancy rate was higher for the short-term than for the long-term protocol, and for intrauterine than for cervical AI (Table 1). The highest pregnancy rate was achieved with short-term protocol using intrauterine AI (54.8%, 51/93), and the lowest response was obtained with long-term protocol using cervical AI (33.7%, 35/104; P < 0.05). These data were not different from data of short-term protocol using cervical AI or long-term protocol using intrauterine AI (42.4%, 36/85; and 44.3% 31/70, respectively). Ewes in estrus/treated ewes was not different among short-term and long-term protocols (83.7%, 149/178; and 82.8%, 144/174, respectively; P = NS). In summary, regardless of insemination technique, short-term protocol of 6 d enhances pregnancy rate in fixed-time AI programs in sheep. Table 1.Main effects of short-term (6 d) v. long-term (14 d) protocol using cervical or intrauterine fixed-time AI on pregnancy rate in sheep Financially supported by Pfizer, SP, Brazil.

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