Considerations on economic forecasting: method developed in the bulletin of EU and US inflation and macroeconomic analysis

This article presents economic forecasting as an activity acquiring full significance when it is involved in a decision-making process. The activity requires a sequence of functions consisting of gathering and organising data, the construction of econometric models and ongoing forecast evaluations to maintain a continuous process involving correction, perfecting and enlarging the data set and the econometric models used, systematically improving forecasting accuracy. With this approach, economic forecasting is an activity based on econometric models and statistical methods, applied economic research with all its general problems. One of these is related to economic data. The widespread belief that if economic information is published, it is valid fo

Scalable visualisation methods for modern Generalized Additive Models

In the last two decades the growth of computational resources has made it possible to handle Generalized Additive Models (GAMs) that formerly were too costly for serious applications. However, the growth in model complexity has not been matched by improved visualisations for model development and results presentation. Motivated by an industrial application in electricity load forecasting, we identify the areas where the lack of modern visualisation tools for GAMs is particularly severe, and we address the shortcomings of existing methods by proposing a set of visual tools that a) are fast enough for interactive use, b) exploit the additive structure of GAMs, c) scale to large data sets and d) can be used in conjunction with a wide range of response distributions. All the new visual methods proposed in this work are implemented by the mgcViz R package, which can be found on the Comprehensive R Archive Network

CONSIDERATIONS ON ECONOMIC FORECASTING: METHOD DEVELOPED IN THE BULLETIN OF EU and US INFLATION AND MACROECONOMIC ANALYSIS

This article presents economic forecasting as an activity acquiring full significance when it is involved in a decision-making process. The activity requires a sequence of functions consisting of gathering and organising data, the construction of econometric models and ongoing forecast evaluations to maintain a continuous process involving correction, perfecting and enlarging the data set and the econometric models used, systematically improving forecasting accuracy. With this approach, economic forecasting is an activity based on econometric models and statistical methods, applied economic research with all its general problems. One of these is related to economic data. The widespread belief that if economic information is published, it is valid for

Time Series Analysis

We provide a concise overview of time series analysis in the time and frequency domains, with lots of references for further reading.time series analysis, time domain, frequency domain

Time Series Analysis

We provide a concise overview of time series analysis in the time and frequency domains, with lots of references for further reading.time series analysis, time domain, frequency domain, Research Methods/ Statistical Methods,

Multi-time-horizon Solar Forecasting Using Recurrent Neural Network

The non-stationarity characteristic of the solar power renders traditional point forecasting methods to be less useful due to large prediction errors. This results in increased uncertainties in the grid operation, thereby negatively affecting the reliability and increased cost of operation. This research paper proposes a unified architecture for multi-time-horizon predictions for short and long-term solar forecasting using Recurrent Neural Networks (RNN). The paper describes an end-to-end pipeline to implement the architecture along with the methods to test and validate the performance of the prediction model. The results demonstrate that the proposed method based on the unified architecture is effective for multi-horizon solar forecasting and achieves a lower root-mean-squared prediction error compared to the previous best-performing methods which use one model for each time-horizon. The proposed method enables multi-horizon forecasts with real-time inputs, which have a high potential for practical applications in the evolving smart grid.Comment: Accepted at: IEEE Energy Conversion Congress and Exposition (ECCE 2018), 7 pages, 5 figures, code available: sakshi-mishra.github.i

Short-term forecasting of electricity consumption using Gaussian processes

Forecasting of electricity consumption is considered as one of the most signi cant aspect of e ective management of power systems. On a long term basis, it allows decision makers of a power supplying company to decide when to build new power plants, transmission and distri- bution networks. On a short term basis, it can be used to allocate resources in a power grid to supply the demand continuously. Forecasting is basically divided into three categories : short-term, medium-term, and long- term. Short-term refers to an hour to a week forecast, while medium-term refers to a week to a year, and predictions that run more than a year refers to long-term. In this thesis, we forecast electricity consumption on a short-term basis for a particular region in Norway using a relatively novel approach: Gaussian process. We design the best feature vector suitable for forecasting electricity consumption using various factors such as previous consumptions, temperature, days of the week and hour of the day. Moreover, feature space is scaled and reduced using reduction and normalization methods, and di erent target variables are analysed to obtain better accuracy. Furthermore, GP is compared with two traditional forecasting techniques : Multiple Back- Propagation Neural Networks (MBPNN), and Multiple Linear Regression (MLR). Finally we show that GP is as better as MBPNN and far better than MLR using empirical results