#Time Series Analysis and Signal Processing Library
This library offers a comprehensive suite of tools for time series analysis and signal processing, leveraging the power of NumPy and Numba for high performance computations. It includes functionalities for datetime manipulation, signal generation, buffer updates, moving averages, and much more. Designed for researchers and engineers working in data analysis, finance, or signal processing, this library streamlines complex numerical computations and analyses.
- Datetime Conversion: Convert between numpy.datetime64 objects and seconds since the Unix epoch.
- Signal Generation: Create sinusoidal signals with specified parameters.
- Buffer Management: Efficiently manage data buffers with functions to update, tick, and perform operations on buffered data.
- Moving Averages: Calculate various forms of moving averages, including Exponential Moving Average (EMA) and Normalized EMA (NEMA).
- Difference and Slope Calculations: Compute differences, slopes, and logarithmic returns from sequential data points.
- Statistical Functions: Implementations of z-score, mean squared displacement (MSD), and correlation calculations.
- Linear Regression: Perform linear regression on data points to find relationships between variables.
- Outlier Detection: Identify outliers based on value differences, slope, and other criteria.
- Data Downsampling: Reduce data resolution while preserving the integrity of the original signal.
To use this library, ensure that you have Python installed on your system along with the numpy
and numba
packages. You can install these dependencies using pip:
pip install numpy numba
import numpy as np
import numba as nb
from your_library_name import * # Replace 'your_library_name' with the name of this library file
dt64 = np.datetime64('2024-01-01 08:00:00')
seconds = to_utc_seconds(dt64)
print(f"UTC Seconds: {seconds}")
dt64_back = from_utc_seconds(seconds)
print(f"Back to Datetime: {dt64_back}")
n = 100 # Number of points
step = 1 # Step size
amplitude = 1.0 # Amplitude of the sinusoid
shift = 0 # Phase shift
frequency = 0.1 # Frequency of the sinusoid
times, values = create_sinusoid(n, step, amplitude, shift, frequency)
buffer = np.zeros(10) # Example buffer
increment = 5
original_value = update_buffer(buffer, increment)
print(f"Original value: {original_value}")
tau = 10.0 # Time constant for EMA
inter = 0 # Interpolation method
t = 1.0 # Current time
z = 2.0 # Current value
time, ema_value = ema(tau, inter, buffer, t, z)
print(f"EMA Value: {ema_value}")
tau = 10.0
inter = 0
n = 100
buffer = np.zeros((15, 3)) # Example buffer setup
t = 1.0
x = np.random.rand(100)
y = 2.0 * x + np.random.normal(size=100) # Generating a simple linear relationship with noise
beta = linear_regression(tau, inter, n, buffer.flatten(), t, x, y)
print(f"Beta Value: {beta}")
Contributions to this library are welcome. Please ensure that any pull requests or issues adhere to the project's coding standards and provide tests for new functionalities.
This library is licensed under the MIT License. See the LICENSE file for more details.