<p>It is hard to provide you with a detailed answer without knowing what you are trying to achieve.</p>
<p>In a nutshell, you first need to decide whether you want to apply a <strong>discrete</strong> (DWT) or a <strong>continous</strong> (CWT) wavelet transform to your time series.</p>
<p>A DWT will allow you to decompose your input data into a set of discrete levels, providing you with information about the frequency content of the signal <em>i.e.</em> determining whether the signal contains high frequency variations or low frequency trends. Think of it as applying several band-pass filters to your input data.</p>
<p>I do not think that you should apply a DWT to your entire time series at once. Since you are working with financial data, maybe decomposing your input signal into 1-day windows and applying a DWT on these subsets would do the trick for you.</p>
<p>In any case, I would suggest:</p>
<ul>
<li>Installing the <code>pywt</code> toolbox and playing with a dummy time series to understand how wavelet decomposition works.</li>
<li>Checking out the abundant literature available about wavelet analysis of financial data. For instance, if you are interested into financial time series forecasting, you might want to read <a href="https://arxiv.org/pdf/1605.07278.pdf" rel="nofollow noreferrer">this paper</a>.</li>
<li>Posting your future questions on the <a href="https://dsp.stackexchange.com/">DSP stack exchange</a>, unless you have a specific coding-related answer.</li>
</ul>
Wavelet for time series
<p>I am trying to use wavelets coefficients as feature for neural networks on a time series data and I am bit confused on usage of the same. Do I need to find the coefficients on entire time series at once, or use a sliding window for finding the same. I mean, will finding coefficients on entire time series for once, include the future data points while determining those coefficients? What should be the approach to go about using Wavelets on a time series data without look ahead bias if any?</p>