Contents
How to use time series to predict electricity consumption?
Since we are now familiar with a basic flow of solving a time series problem, let us get to the implementation. The dataset can be downloaded from here. It contains only 2 columns, one column is Date and the other column relates to the consumption percentage. It shows the consumption of electricity from 1985 till 2018.
How can machine learning be used to predict electricity consumption?
This data represents a multivariate time series of power-related variables that in turn could be used to model and even forecast future electricity consumption. Machine learning algorithms predict a single value and cannot be used directly for multi-step forecasting.
How is electricity consumption data used in forecasting?
This data represents a multivariate time series of power-related variables, that in turn could be used to model and even forecast future electricity consumption. In this tutorial, you will discover a household power consumption dataset for multi-step time series forecasting and how to better understand the raw data using exploratory analysis.
When was the household power consumption data collected?
The data was collected between December 2006 and November 2010 and observations of power consumption within the household were collected every minute. It is a multivariate series comprised of seven variables (besides the date and time); they are:
How is time series analysis used to predict future events?
Time series forecasting is a technique for the prediction of events through a sequence of time. The technique is used across many fields of study, from geology to behavior to economics. The techniques predict future events by analyzing the trends of the past, on the assumption that future trends will hold similar to historical trends.
Which is a measure of volatility for a time series?
This can be a measure for a single time series or a relative measure comparing multiple time series together. Let’s assume a Dickey-Fuller test has already been conducted, and all the time series do not have a unit root.
Which is the best metric for time series data?
Hyndman’s contention is that his MASE metric is optimal for time series data. MASE is a normalized loss function. After creating train and test data, the test data residuals are normalized or divided by the average error in the training data.
What is the use of time series forecasting?
Time series forecasting is performed in a variety of applications including: Time series forecasting is sometimes just the analysis of experts studying a field and offering their predictions. In many modern applications, however, time series forecasting uses computer technologies, including:
How are systematic methods used in load forecasting?
Systematic methods are adopted in order to quantitatively define future loads. Advanced methods of forecasting involves Time series modelling (extrapolation) and Regression analysis (Correlation) Time series uses the previous data for determining future values. It is like extrapolation of curves.
What are the goals of time series analysis?
There are two main goals of time series analysis: (a) identifying the nature of the phenomenon represented by the sequence of observations, and (b) forecasting (predicting future values of the time series variable). Both of these goals require that the pattern of observed time series data is identified and more or less formally described.