I have a Unix time series (x) with an associated signal value (y) which is generated every minute, dropping the first value and appending a new one. I am trying to smooth the resulting curve without loosing time accuracy with a specific emphasis on the final value of the smoothed curve which will be written to a database. I would like to be able to adjust the smoothing to a considerable degree.
I have studied (as mathematical layman, more or less) all options I could find and I could master. I came across Savitzki Golay which looked perfect until I realized it works well on past data but fails to produce a reliable final value if no future data is available for smoothing. I have tried many other methods which produced results but could not be adjusted like Savgol.
import pandas as pd
from bokeh.plotting import figure, show, output_file
from bokeh.layouts import column
from math import pi
from scipy.signal import savgol_filter
from sklearn.preprocessing import PolynomialFeatures
from sklearn.linear_model import LinearRegression
from scipy.interpolate import splrep, splev
from scipy.ndimage import gaussian_filter1d
from scipy.signal import lfilter
from scipy.interpolate import UnivariateSpline
import matplotlib.pyplot as plt
df_sim = pd.read_csv("/home/20190905_Signal_Smooth_Test.csv")
#sklearn Polynomial*****************************************
poly = PolynomialFeatures(degree=4)
X = df_sim.iloc[:, 0:1].values
print(X)
y = df_sim.iloc[:, 1].values
print(y)
X_poly = poly.fit_transform(X)
poly.fit(X_poly, y)
lin2 = LinearRegression()
lin2.fit(X_poly, y)
# Visualising the Polynomial Regression results
plt.scatter(X, y, color='blue')
plt.plot(X, lin2.predict(poly.fit_transform(X)), color='red')
plt.title('Polynomial Regression')
plt.xlabel('Time')
plt.ylabel('Signal')
plt.show()
#scipy interpolate********************************************
bspl = splrep(df_sim['timestamp'], df_sim['signal'], s=5)
bspl_y = splev(df_sim['timestamp'], bspl)
df_sim['signal_spline'] = bspl_y
#scipy gaussian filter****************************************
smooth = gaussian_filter1d(df_sim['signal'], 3)
df_sim['signal_gauss'] = smooth
#scipy lfilter************************************************
n = 5 # the larger n is, the smoother curve will be
b = [1.0 / n] * n
a = 1
histo_filter = lfilter(b, a, df_sim['signal'])
df_sim['signal_lfilter'] = histo_filter
print(df_sim)
#scipy UnivariateSpline**************************************
s = UnivariateSpline(df_sim['timestamp'], df_sim['signal'], s=5)
xs = df_sim['timestamp']
ys = s(xs)
df_sim['signal_univariante'] = ys
#scipy savgol filter****************************************
sg = savgol_filter(df_sim['signal'], 11, 3)
df_sim['signal_savgol'] = sg
df_sim['date'] = pd.to_datetime(df_sim['timestamp'], unit='s')
#plotting it all********************************************
print(df_sim)
w = 60000
TOOLS = "pan,wheel_zoom,box_zoom,reset,save"
p = figure(x_axis_type="datetime", tools=TOOLS, plot_width=1000, plot_height=250,
title=f"Various Signals y vs Timestamp x")
p.xaxis.major_label_orientation = pi / 4
p.grid.grid_line_alpha = 0.9
p.line(x=df_sim['date'], y=df_sim['signal'], color='green')
p.line(x=df_sim['date'], y=df_sim['signal_spline'], color='blue')
p.line(x=df_sim['date'], y=df_sim['signal_gauss'], color='red')
p.line(x=df_sim['date'], y=df_sim['signal_lfilter'], color='magenta')
p.line(x=df_sim['date'], y=df_sim['signal_univariante'], color='yellow')
p1 = figure(x_axis_type="datetime", tools=TOOLS, plot_width=1000, plot_height=250,
title=f"Savgol vs Signal")
p1.xaxis.major_label_orientation = pi / 4
p1.grid.grid_line_alpha = 0.9
p1.line(x=df_sim['date'], y=df_sim['signal'], color='green')
p1.line(x=df_sim['date'], y=df_sim['signal_savgol'], color='blue')
output_file("signal.html", title="Signal Test")
show(column(p, p1)) # open a browser
I expect a result that is similar to Savitzky Golay but with valid final smoothed values for the data series. None of the other methods present the same flexibility to adjust the grade of smoothing. Most other methods shift the curve to the right. I can provide to csv file for testing.
This really depends on why you are smoothing the data. Every smoothing method will have side effects, such as letting some 'noise' through more than other. Research 'phase response of filtering'.
A common technique to avoid the problem of missing data at the end of a symmetric filter is to just forecast your data a few points ahead and use that. For example, if you are using a 5-term moving average filter you will be missing 2 data points when you go to calculate your end value.
To forecast these two points, you could use the auto_arima() function from the pmdarima module, or look at the fbprophet module (which I find quite good for this kind of situation).