Project

time_wise

0.0
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TimeWise provides a robust set of tools for time series analysis in Ruby, including basic statistical functions, moving averages, and visualization capabilities.
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 Popularity
Downloads
184
Stars
2
Forks
0
Watchers
1
 Releases
Current version
0.1.0
Total releases
1
First release
2025-03-23
Latest release
2025-03-23
 Development
Primary Language
Ruby
Licenses
MIT
Average date of last 50 commits
2025-03-23
Reverse Dependencies
0

 Dependencies

Development

rake
~> 13.0
rspec
~> 3.0
rubocop
~> 1.21

Runtime

ascii_charts
~> 0.9.1
numo-narray
~> 0.9.2
 Project Readme

TimeWise

TimeWise is a Ruby gem for time series analysis and visualization. It offers intuitive tools for working with time-dependent data, perfect for e-commerce, healthcare, sports analytics, and more.

Features

  • Core Statistical Functions: Calculate mean, median, standard deviation, variance, skewness, kurtosis, etc.
  • Moving Averages: Simple, weighted, exponential, and advanced smoothing techniques
  • Data Visualization: Basic visualization capabilities for analyzing trends
  • Time Series Operations: Slicing, manipulating, and analyzing time-indexed data

Installation

Add this line to your application's Gemfile:

gem 'time_wise'

And then execute:

$ bundle install

Or install it yourself as:

$ gem install time_wise

Usage

Creating a Time Series

require 'time_wise'

# Create a time series from an array of values
data = [100, 110, 120, 115, 125, 130, 140]
ts = TimeWise.create(data)

# With dates
dates = [
  Date.new(2023, 1, 1),
  Date.new(2023, 1, 2),
  Date.new(2023, 1, 3),
  Date.new(2023, 1, 4),
  Date.new(2023, 1, 5),
  Date.new(2023, 1, 6),
  Date.new(2023, 1, 7)
]
ts_with_dates = TimeWise.create(data, dates)

# Load from CSV
ts_from_csv = TimeWise.load_csv('data.csv', 'value', 'date')

Basic Statistics

# Calculate basic statistics
mean = ts.stats.mean
median = ts.stats.median
std_dev = ts.stats.std_dev

# Get a summary of all statistics
summary = ts.stats.summary
puts summary[:mean]
puts summary[:std_dev]

# Calculate autocorrelation
acf = ts.stats.autocorrelation(5)  # For lags 0-5

# Calculate correlation between two time series
correlation = ts1.stats.correlation(ts2)

Moving Averages

# Simple moving average with window size 3
sma = ts.moving_average.simple(3)

# Exponential moving average with alpha=0.2
ema = ts.moving_average.exponential(0.2)

# Weighted moving average with custom weights
weights = [0.1, 0.2, 0.3, 0.4]
wma = ts.moving_average.weighted(4, weights)

# Double exponential smoothing (Holt's method)
dema = ts.moving_average.double_exponential(0.2, 0.1)

Visualization

# Generate a line chart
ts.plot.line_chart(title: "My Time Series")

# Compare two time series
ts.plot.comparison_chart(another_ts, title: "Comparison")

# Export to CSV
ts.plot.export_csv("output.csv")

# Display data as a table
puts ts.plot.data_table(10)  # Show first 10 rows

Coming Soon (in Future Releases)

  • ARIMA Models: For advanced time series forecasting
  • Seasonal Analysis: Tools for detecting and working with seasonal patterns
  • Advanced Visualization: More chart types and interactive plotting
  • Anomaly Detection: Methods for identifying outliers and unusual patterns

Development

After checking out the repo, run bin/setup to install dependencies. Then, run rake spec to run the tests. You can also run bin/console for an interactive prompt that will allow you to experiment.

Contributing

Bug reports and pull requests are welcome on GitHub at https://github.com/yourusername/time_wise.

License

The gem is available as open source under the terms of the MIT License.