TimeSeriesAnalyzer

Testing

TimeSeriesAnalyzer is designed for solving and visualizing numerical series

Installation

gem install TimeSeriesAnalyzer

The rmagick gem must be installed for it to work properly https://imagemagick.org/script/download.php#windows

Then you need to go to the project terminal

set CPATH="C:\Program Files (x86)\ImageMagick-[VERSION]-Q16\include"
set LIBRARY_PATH="C:\Program Files (x86)\ImageMagick-[VERSION]-Q16\lib"
em install rmagick

Usage

Uploading data from a CSV file:

time_series = TimeSeriesAnalyzer::TimeSeries.load_from_csv(file_path)
# the second variable "period" determines the seasonality of the data
# by default -1, which means the seasonality is determined automatically

Visualization of a time series:

time_series.plot(file_name)
# the second variable "title" indicates the title of the graph

Decomposition of a time series:

decomposed = time_series.decompose

print(decomposed[:trend].data) # trend
puts ""
print(decomposed[:seasonal].data) # seasonal component
puts ""
print(decomposed[:residual].data) # remains
puts ""

decomposed[:trend].plot('trend.png')
decomposed[:seasonal].plot('seasonal.png')
decomposed[:residual].plot('residual.png')

Application of the moving average:

moving_average(window_size)
#Returns a numeric series representing the moving average of the original
#parameter window_size is the size


smoothed_series = time_series.moving_average(3)
print(smoothed_series.data)
puts ""
smoothed_series.plot('moving_average.png')

Applying exponential smoothing:

exponential_smoothing(alpha)
#Returns a numeric series representing the exponential smoothing of the original one
#The value of alpha represents the smoothing factor, which determines the weight of the current
#observation compared to the smoothed previous value.


smoothed_series = time_series.exponential_smoothing(0.3)
print(smoothed_series.data)
puts ""
smoothed_series.plot('exponential_smoothing.png')

Anomaly detection:

detect_anomalies
#Function that returns detected anomalies in the numeric series
#Anomalies found are returned as dictionaries with timestamps and values.

anomalies = time_series.detect_anomalies
anomalies.each { |anomaly| puts "Anomaly detected at #{anomaly[:timestamp]}: #{anomaly[:value]}" }

Forecasting:

forecast(steps)
#Function that predicts the following values (prediction based on the subsequent change of residues only)
#steps - number of steps for which it is necessary to forecast

forecasted_values = time_series.forecast(10)
puts "Forecasted values: #{forecasted_values}"

Contributing

Bug reports and pull requests are welcome on GitHub at https://github.com/KuryataDanil/TimeSeriesAnalyzer. This project is intended to be a safe, welcoming space for collaboration, and contributors are expected to adhere to the code of conduct.

License

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

Code of Conduct

Everyone interacting in the TimeSeriesAnalyzer project's codebases, issue trackers, chat rooms and mailing lists is expected to follow the code of conduct.