#Electricity pricing splice 2 dataset download series
For example, if the series is consistently increasing over time, the sample mean and variance will grow with the size of the sample, and they will always underestimate the mean and variance in future periods. Such statistics are useful as descriptors of future behavior only if the series is stationary. A stationarised series is relatively easy to predict: you simply predict that its statistical properties will be the same in the future as they have been in the past! Another reason for trying to stationarise a time series is to be able to obtain meaningful sample statistics such as means, variances, and correlations with other variables. Most statistical forecasting methods are based on the assumption that the time series can be rendered approximately stationary (i.e., “stationarised”) through the use of mathematical transformations. You can say that this is more a type of exploratory analysis of time series data.Ī stationary time series is one whose statistical properties such as mean, variance, autocorrelation, etc. Do not worry about these terms right now, as we will discuss them during implementation. We try to identify all the underlying patterns related to the series like trend and seasonality. In this step, we try to visualize the series. We will also look at the python implementation of each stage of our problem-solving journey.
We will understand about tasks which one needs to perform in every stage.
A simple/basic journey of solving a time series problem can be demonstrated through the following processes. Solving a time series problem is a little different as compared to a regular modeling task. Regardless of the depth of our understanding and the validity of our interpretation (theory) of the phenomenon, we can extrapolate the identified pattern to predict future events. Once the pattern is established, we can interpret and integrate it with other data (i.e., use it in our theory of the investigated phenomenon, e.g., seasonal commodity prices). Both of these goals require that the pattern of observed time series data is identified and more or less formally described. 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). In many modern applications, however, time series forecasting uses computer technologies, including:
Time series forecasting is sometimes just the analysis of experts studying a field and offering their predictions.