Time-Series Forecasting

By C. Borkowf. 2002

924 citations recorded by the source corpus.

How researchers see this paper

The researchers whose trusted networks rank it highest, strongest advocate first. Every researcher gets a different order.

In Signal Processing

Citations in the corpus

Think Globally, Act Locally: A Deep Neural Network Approach to High-Dimensional Time Series Forecasting (2019, methodology): “For a comprehensive treatment of traditional methods, we refer the readers to [13, 19, 4, 18, 12] and the references there in.”
Online Learning: A Comprehensive Survey (2018, background): “Time series analysis and prediction [241, 242, 243, 244] is a classical problem in machine learning, statistics, and data mining.”
Model identification of ARIMA family using genetic algorithms (2005, background): “Although many papers [3,6–9,36] concentrate on model estimation, the main problem is assessing the order of the process, rather than estimating the coefficients [37].”
Functional time series approach for forecasting very short-term electricity demand (2013, background): “Interval forecasts are important for assessing the probabilistic uncertainty associated with point forecasts [7,8].”
Recent advances in electricity price forecasting: A review of probabilistic forecasting (2016, background): “And probabilistic forecasting has a lot to offer, in particular, improved assessment of future uncertainty, ability to plan different strategies for the range of possible outcomes, increased effectiveness of submitted bids and possibility of more thorough forecast comparisons [1, 8, 9].”
Stock trend forecasting in turbulent market periods using neuro-fuzzy systems (2015, background): “As such, we deal with a time series forecasting problem (Chatﬁeld 2013; Paulescu et al. 2013).”
Precision, bias, and uncertainty for state population forecasts: an exploratory analysis of time series models (2007, background): “The long-term dynamics of ARIMA models are generally controlled more by the differencing term (d) than by the values of the autoregressive and moving average terms (p and q), which have their greatest impact on short-term dynamics (Chatfield 2000).”
Econometric Forecasting (2007, methodology): “Once the rank has been determined, estimation of (5) is conducted by reduced-rank regression, a technique that was developed by T.”