Raghvendra Tiwari

Temporal disaggregation is a method of disaggregating low frequency time series into higher-frequency time series. A major challenge faced by many when working with time series data is the non-availability of data at the required granularity. One might come across instances where the data is monthly or quarterly, but the preference is for the data to be weekly, daily, etc. Commonly used techniques to estimate the missing values often fail to capture the behavior of the underlying high-frequency data. This paper introduces a novel approach to tackling this problem. We tried to use a statistical gaussian-interpolation technique to tackle the problem. Gaussian Process is the non-parametric method that tries to model the functional form of time series using a correlation matrix known as the kernel function. It not only models various crucial components of time series which are trend, seasonality, and stochasticity but also can be tuned to avoid overfitting and smoothen the time series using appropriate kernel functions which could be a problem with the naïve approaches such as interpolation which tries to fit a certain polynomial function. Since it is a statistical model every predicted/filled value there is an associated probability which gives the confidence of being an instance of the parent time series. We will also highlight how our method compares to the other commonly used method and instances where it can be deployed without risk. Often, some time-series datasets are rendered useless in our analysis/model due to the lack of availability of data at the preferred granularity. It is also sometimes not feasible to capture the data at a given frequency or only some sample data at the frequency it is available. Our method deals to tackle exactly those problems by providing an accurate estimate for the underlying data.