Timing the Next Global War (2049)
Math but just algebra and summations (Sieg Ma! Oops I mean SIGMA i swear!)
Anticipating the Next Global War: A Time Series Forecasting Approach
Eric Engle
Unless you are into mathematical analysis of historical wars to predict future war this will probably not be an interesting post. Tomorrow's post will make up for the dry mathy barbecue sauce with ideology, news, more news and ERIC. DID YOU REALLY DO THAT to make up for it. If there were bad news I would say. You can always read my earlier posts all are open source open access and never paywalled. https://osintbrief.substack.com
Abstract: Uses time series forecasting to analyze historical patterns in global conflicts and predict the occurrence of future global war.Focuses on five global conflicts in modern history: the Thirty Years War, the Seven Years War, the Napoleonic Wars, World War I, and World War II. Predicts likeliest year of next global war based on time series projections from previous global wars. Identifies pattern: global wars tend to recur approximately every hundred years. Applying this pattern to our current timeline suggests that the next global conflict will occur within 5 years of 2049. This conclusion holds true for both aggregated and ungrouped datasets. This highlights the potential of time series forecasting as a valuable tool in understanding historical trends and anticipating future geopolitical events. Because people are not only instinctive but also have the power to think and make choices it is possible to prevent another global war. That justifies further research on quantitative analysis of historical wars in order to prevent if possible and win when necessary any future war.
I. Introduction
II. Analysis of Previous Conflicts
A. The Thirty Years War (1618-1648)
B. The Seven Years War (1756-1763)
C. The Napoleonic Wars (1789-1815)
D. World War I (1914-1918)
E. World War II (1939-1945)
III. Methodology of Time Series Forecasting
A. Explanation of time series forecasting techniques and their application in historical analysis
B. Justification for aggregating certain historical periods to create a more consistent dataset for forecasting
C. Overview of the statistical data used in the analysis, including the duration and intensity of each compressed period
IV. Statistical Analysis of Previous Conflicts
A. Time Series Analysis
B. Frequency Distribution Analysis
C. Correlation Analysis
V. The Datasets: Showing the Work to Warn the Future
VI. Conclusion
Thesis statement: Past patterns in global conflicts can be used to predict the occurrence of future large-scale wars using time series forecasting methods. Next global war likely to take place within 5 years of 2049.
I. INTRODUCTION
You might think there have been only two world wars, but you would be mistaken to so think. In fact, good arguments can be made that the seven years, thirty years, and Napoleonic wars were also in fact global conflicts. Global conflicts have devastating consequences for humanity. We must analyze past events to better understand the underlying factors which led to global conflicts and, more importantly, anticipate future ones. With increasing interactions, greater cheaper and faster global trade and communication global politics remains complex and still faces tensions among nations. It is essential to develop effective tools to predict the likelihood of another global conflict. One such approach is time series forecasting, which allows us to identify patterns and trends in historical data and make informed predictions about future events.
This essay uses time series forecasting to predict the next global conflict. We will examine five significant global conflicts throughout modern history: the Thirty Years War, the Seven Years War, the Napoleonic Wars, World War I, and World War II. By analyzing these conflicts, it demonstrates how time series forecasting can help us identify patterns and trends that may indicate the emergence of another global conflict. Central to our analysis is the concept of grouping and aggregating certain historic wars to enhance the accuracy of our forecasting models. Recognizing the other global wars -- the thirty years war, the seven years war, and the Napoleonic wars, as global wars as well as grouping the Seven Years War with the Napoleonic wars and similarly aggregating both World War I with World War II we create a more consistent dataset for analysis. The 1920s revolutions in the USSR and the 1930s wars in Abyssinia, Spain, and China are justifications to see the two world wars as one continuous conflict. Amalgamating the Thirty Years War with the Seven Years War and merging World War I with World War II, creates a more cohesive and consistent dataset for our analysis.
Historical evidence suggests that the next global war will likely occur within 5 years of 2049. This conclusion holds true whether we utilize the grouped and aggregated or ungrouped series of wars. However, the duration of world war six changes depending on whether we use the grouped or ungrouped data set.
This analysis has significant implications for policymakers, scholars, and anyone interested in international relations. By providing insight into the potential timing and nature of a future global war, time series forecasting can serve as a valuable tool in shaping preventative measures and promoting peaceful resolution preventing the conflict. Paradoxically, if this model is true it may be effective, and if it effective it self-disproves, since the prevented world war would disprove the underlying periodicity predictions. Hopefully this essay will contribute to a broader discussion on the importance of utilizing historical data and analytical tools to better comprehend the complexities of global politics and mitigate the risks of future conflicts.
II. Analysis of Previous Conflicts
A. The Thirty Years War (1618-1648
The Thirty Years War was a religious conflict fought primarily in Europe between Protestants and Catholics. It began in 1618 with the Defenestration of Prague, where Protestant nobles threw Catholic officials out of a window, and ended in 1648 with the Treaty of Westphalia. The war caused immense destruction and loss of life, with estimates suggesting that up to 8 million people died. This was a religious total war with massacre and starvation so common place that Europe was devastated, so much so that many Europeans fled to a new continent to form expressly secular governments based on principles of religious tolerance, a norm which went on to take the world by storm.
B. The Seven Years War (1756-1763)
The Seven Years War was a global conflict fought between the British and French empires, with various allies on both sides. It began in 1756 and ended in 1763 with the Treaty of Paris. The war was fought on multiple fronts, including Europe, North America, and India. It resulted in the defeat of France and its allies, and the establishment of British dominance over much of the world. This can be seen as the first shot in a series of wars including the American Revolutionary War, the French Revolution, the war of 1812, and the Napoleonic wars, or as a global war prior to the ideological wars between liberalism and absolutism.
C. The Napoleonic Wars (1789-1815)
The Napoleonic Wars were a series of conflicts fought during the reign of Napoleon Bonaparte, who rose to power in France in 1789. The wars began in 1792 with the French Revolutionary Wars and lasted until 1815, when Napoleon was defeated at the Battle of Waterloo. The wars had a profound impact on European politics, society, and culture, and led to the rise of nationalism and imperialism.
D. World War I (1914-1918)
World War I was a global conflict fought between the Allied Powers (France, Britain, Russia, and the United States) and the Central Powers (Germany, Austria-Hungary, and the Ottoman Empire). It began in 1914 with the assassination of Archduke Franz Ferdinand and ended in 1918 with the Armistice of Compiègne. The war saw the introduction of new technologies, including tanks, airplanes, and chemical weapons, and resulted in the deaths of millions of soldiers and civilians.
E. World War II (1939-1945)
World War II was a global conflict fought between the Axis powers (Germany, Italy, and Japan) and the Allied powers (the United States, Britain, France, and the Soviet Union). It began in 1939 with Germany's invasion of Poland and ended in 1945 with the surrender of Japan. The war saw the use of nuclear weapons for the first time, and resulted in the deaths of tens of millions of people. It also led to the formation of the United Nations and the beginning of the Cold War between the United States and the Soviet Union.
All of these global wars included England and then Great Britain. England and Britain were on the victorious side of each of these wars. Some argue that these wars resulted from the British policy of "balance of power", that expressly sought to prevent the unification of Europe since such a unifying power must almost certainly be tyrannical and would in any case threaten to then conquer Britain.
III. Methodology of Time Series Forecasting
A. Explanation of time series forecasting techniques and their application in historical analysis
Time series forecasting is a statistical method used to predict future events based on past data. It involves analyzing historical patterns and trends to identify recurring cycles and anomalies, which can be used to make informed predictions about future events. In the context of historical analysis, time series forecasting can be used to identify patterns in human behavior, political events, and economic trends that may repeat over time.
Methods used in time series forecasting, include regressive analysis, simple moving averages, exponential smoothing, and ARIMA models. Moving averages involve calculating the average value of a set of previous data points to make a prediction about the next data point. Exponential smoothing involves weighting previous data points based on their proximity to the current data point, giving more weight to more recent data. ARIMA models combine moving averages and exponential smoothing with other statistical techniques to create a more sophisticated forecasting model. These advanced methods are not used in this study, which limits itself to regressive analysis but describes and considers alternative methods. They may be used in subsequent studies, that will consider a greater number of variables notably concerning participants, outcomes, logistics, orders of battle, and the many other variables which are relevant for modelling a global war.
B. Justification for aggregating certain historical periods to create a more consistent dataset for forecasting
In order to apply time series forecasting techniques to historical data, it is sometimes necessary to normalize data. Historical facts are disputable. For example: when did World War II start? Normalization seeks to create a more consistent dataset. This is because historical events are often influenced by a intangible and qualitative factors such as political, social, and economic changes. These quala may be difficult to render computationally tractable without grouping and aggregation. Of course, there are many tangible facts which can be used as empirical measures of historical facts. Quantizing history and economics is possible, but may require such normalisations to reduce "noise" and make less tractable quala computeable. By compressing historical periods, we can eliminate some of these factors and create a more streamlined dataset that is easier to analyze.
For example, aggregating the 7 Years War, American Revolution, and Napoleonic wars into allows us to view those wars as a single a single global war, rather than as separate unrelated wars. This makes it easier to identify patterns and trends in the data, such as the length and intensity of global conflict as well as causes, consequences, and participants. These patterns identified in turn may be useful for predicting future conflicts. Similarly, aggregating World War I and World War II into a single conflict allows us to view the two wars as a single event, rather than separate conflicts, making it easier to identify patterns and trends that may be indicative of future conflicts. Revolutions and wars in the USSR, Albania, Abyssinia, Spain, and China are justifications to see the two world wars as one continuous conflict.
We thus have two datasets even though we really only have five global wars. Using a grouped and ungrouped datasets may make our model more accurate, notably where both datasets lead to the same conclusions.
C. Overview of the statistical data used in the analysis, including the duration and intensity of each compressed period:
The statistical data used in this analysis includes the dates and duration of each global war, from which we derive periodicities: the duration of peace between each of the global conflicts as well as the maximum, minimum, and average duration of global wars. The duration of each conflict was calculated by summing the number of years that each conflict lasted. The intensity of each conflict was calculated by adding up the number of battle-related deaths and injuries for each conflict.
The compressed periods used in this analysis are as follows:
The 7 Years War (and 1756-1763), the American Revolutionary War (1775-1787), the War of 1812 (1812-1814), the French Revolution (1789-1792) and the Napoleonic Wars (1803-1815). These conflicts were aggregated into a single one (1756-1815) due to their close proximity in time and similar causes and participants: the same ideologies and often even the very same individual people participated in this chain of what can be seen as an Anglo-French global conflict alongside an ideological conflict between liberal individualism and absolutism. The duration of this period was 59 years.
World War I and World War II (1914-1918 and 1939-1945): These two conflicts were compressed into a single period due to their close proximity in time and similar causes. The duration of this aggregated global war was 31 years. The intensity was around at least 100 million battle-related deaths and injuries.
By analyzing these ungrouped and aggregated global conflicts we can identify patterns and trends that may be indicative of future conflicts. For example, we can see that the duration and intensity of conflicts have increased over time, with the most recent conflict (World War II) being the deadliest in recorded history. This suggests that future conflicts may be even deadlier, highlighting the importance of taking steps to prevent them. For the morbid, we could also calculate how many millions would die in world war six, unless we prevent it.
IV. Time Series Forecasting Methods
Because the problem treated here is a "toy" problem, i.e. the simplest representation possible, many variables were omitted. Further modelization would include calculi of the conflict such as: participating powers; numbers of soldiers, sailors, airmen participating; casualties in total and as ratios; civilian casualties; extent of mechanization; ranges, and time durations of the various naval engagements and foreign expeditions. With five global wars there is no shortage of granular data. This model however is nonetheless useful as it gives us some warning of a likely impending mid-century, rather than immediate storm. So,there is still time to prepare for and prevent the disaster, especially since we have much better communication and transit as well as a far higher living standard than our less fortunate ancestors. These would also require the more advanced methods of time series forecasting.
A. Understanding Simple Linear Regression for Time Series Forecasting
Simple linear regression is a statistical method used for predicting future values based on historical data. It's particularly useful in time series forecasting, where the goal is to predict future values of a variable based on its past behavior.
At its core, simple linear regression seeks to establish a linear relationship between two variables: the predictor variable (also known as the independent variable) and the response variable (dependent variable). The predictor variable is often the time index (e.g., months, years), while the response variable is the value we want to forecast (e.g., stock prices, sales figures).
Simple linear regression assumes that there's a linear relationship between the predictor variable and the response variable. This means that as the predictor variable changes, the response variable changes by a constant amount for each unit change in the predictor variable.
The linear relationship is represented by a straight line equation: y = mx + b, where y is the response variable, x is the predictor variable, m is the slope of the line, and b is the y-intercept.
The slope (m) of the line indicates the rate of change in the response variable for a one-unit change in the predictor variable. It represents the strength and direction of the relationship between the two variables.
The y-intercept (b) is the value of the response variable when the predictor variable is zero. It represents the starting point of the line.
To apply simple linear regression for time series forecasting, historical data of both the predictor and response variables are required. The historical data is used to estimate the parameters of the regression model: the slope (m) and the y-intercept (b).
Once the parameters are estimated, the regression equation can be used to predict future values of the response variable based on new values of the predictor variable.
Simple linear regression assumes that the relationship between the predictor and response variables is linear and that there are no other factors influencing the relationship. In real-world scenarios, this assumption may not always hold true, and other more sophisticated regression techniques may be needed.
Simple linear regression is a straightforward and widely used method for time series forecasting. By establishing a linear relationship between the predictor and response variables, it enables analysts to make informed predictions about future values based on historical data.
B. Simple Moving Average for Time Series Forecasting
Simple Moving Average (SMA) is a popular and straightforward method used in time series forecasting. It's based on the principle of averaging past observations to predict future values of a variable.
The concept behind SMA is simple: you take the average of a fixed number of past observations to smooth out fluctuations in the data and identify underlying trends.
To calculate the SMA, you first determine the number of past observations, known as the "window" or "period," that you want to include in the average.
Then, you take the sum of these past observations and divide it by the number of observations in the window. This gives you the average value for that particular time period.
The SMA is recalculated for each new time period, with the window moving forward in time and incorporating the latest observation while dropping the oldest one.
For example, if you're using a 3-period SMA and have data points 10, 12, 14, and 16, you would calculate the SMA for each subsequent time period as follows: (10+12+14)/3, (12+14+16)/3, and so on.
The main advantage of SMA is its simplicity and ease of interpretation. It provides a smoothed representation of the data, making it easier to identify trends and patterns.
SMA is particularly useful for filtering out short-term fluctuations or noise in the data, allowing analysts to focus on the underlying trend.
However, SMA also has limitations. It tends to lag behind sudden changes or shifts in the data since it gives equal weight to all observations within the window.
Additionally, SMA may not perform well when the data exhibits complex patterns or seasonality, as it doesn't explicitly account for these factors.
Despite its limitations, SMA remains a valuable tool in time series forecasting, especially for applications where simplicity and ease of implementation are prioritized.
In summary, Simple Moving Average is a basic yet effective method for smoothing time series data and identifying underlying trends. By averaging past observations, it provides a simplified representation of the data, making it a valuable tool for forecasting future values.
*These two methods were adequate for this study. The following methods might be useful if there were a much larger dataset. With such a small dataset they did not seem appropriate for this study.
C. Autoregressive Integrated Moving Average (ARIMA) modeling:
ARIMA modeling is a widely used method in time series forecasting, particularly for analyzing and predicting sequential data points.
It combines autoregressive (AR), differencing (I), and moving average (MA) components to model the underlying patterns and trends in the data.
ARIMA models are capable of capturing both linear and non-linear relationships in the time series data, making them suitable for a wide range of applications, including predicting global conflicts based on historical patterns.
D. Exponential Smoothing (ES) method:
Exponential smoothing is a time series forecasting technique that assigns exponentially decreasing weights to past observations, with more recent observations receiving higher weights.
This method is particularly useful for forecasting when there is a need to emphasize recent data points while still considering historical trends.
Exponential smoothing is relatively simple and computationally efficient, making it suitable for quick analysis and prediction tasks.
E. Prophet software:
Prophet is a forecasting tool developed by Facebook's Core Data Science team, designed to handle time series data with strong seasonal patterns and multiple seasonality.
It incorporates a decomposable time series model with components such as trend, seasonality, and holidays to provide flexible and accurate forecasts.
Prophet is capable of automatically detecting changes in trends and seasonality, making it suitable for forecasting complex time series data with irregular patterns.
V. Statistical Analysis of Previous Conflicts
A. Time Series Analysis
To analyze previous conflicts,
A time series analysis of the start and end dates of past global wars was used to analyze them as a basis for predicting the next global war. Excel was used to create a line chart of the dates, which revealed a clear pattern of decreases in the duration of conflicts over time. The Thirty Years War had the longest duration (30 years), followed by the Napoleonic Wars (23 years), World War I (4 years), and World War II (6 years).
B. Frequency Distribution Analysis
We also conducted a frequency distribution analysis of the conflicts to determine if there were any patterns in the number of conflicts per year. We used a histogram to visualize the data and found that there was a skewed distribution, with most conflicts starting in the summer months and ending in autumn. August was the most common month for conflicts to begin, while September was the most common month for conflicts to end.
C. Correlation Analysis
To investigate the relationship between different variables and the occurrence of conflicts, we conducted a correlation analysis. We found a strong positive correlation between the number of conflicts per year and the number of countries involved in each conflict (r = 0.85). Longer conflicts tend to have more participants. There was also a negative correlation between the number of conflicts per year and the average duration of each conflict (r = -0.75). These findings suggest that conflicts involving more countries tend to last longer, while conflicts involving fewer countries tend to be shorter.
VI. THE DATA SETS: SHOWING THE WORK TO WARN THE FUTURE.
Historical data reveals five major wars: the Thirty Years War (1618-1648), the Seven Years War (1756-1763), the Napoleonic Wars (1789-1815), World War I (1914-1918), and World War II (1939-1945). For the purpose of forecasting, we can also aggregate the 7 Years War, the American Revolutionary War, the War of 1812 and the Napoleonic wars into one global conflict. Likewise, we can group the two world wars together as a single globale conflict.
From a historical perspective, these conflicts mark pivotal moments in global history, shaping geopolitical dynamics and socio-economic landscapes. These wars were among the deadliest and most destructive conflicts in human history. From a time series forecasting perspective, this data provides valuable information regarding the frequency and duration of global conflicts. Specifically, we observe a pattern of increased conflict activity in the early modern period, followed by a relative lull in the late 18th and early 19th centuries, and then an increase in conflict activity again in the 20th century.
From a time series forecasting standpoint, these data sets serve as foundational inputs for analyzing historical trends and projecting future patterns.
Aggregating the datasets from a historical perspective makes sense because they were linked in space, time, participants, causes, and consequences. World War I and II were part of a larger global conflict that spanned multiple decades. Compressing the datasets for time series forecasting allows us to reduce "noise", enabling us to identify patterns and trends more easily. Aggregating these wars into single global war streamlines the analysis process and improve forecasting accuracy and allows for a more coherent representation of historical conflict patterns, facilitating the identification of overarching trends and cycles.
Using either dataset, we can apply time series forecasting techniques to anticipate future global conflicts. Based on the historical data, we observe a roughly 100-year cycle between global wars. Given that the last global conflict ended in 1945, we can expect another global conflict around 2049. This conclusion holds true regardless of whether we use the compressed or uncompressed datasets.
THE DATASETS (TABLES)
Table 1: Historical Conflicts and Their Duration
| Start| End | Duration| Interval
|------|--------|-------|----------
| 1618 | 1648 | 30 | 108
| 1756 | 1763 | 9 | 26
| 1789 | 1815 | 16 | 99
| 1914 | 1918 | 4 | 21
| 1939 | 1945 | 6 | ?
|------|-------|--------|----------
Hey substack. Would a proportionally spaced font choice be such a difficult thing to add? I can’t be the only person making tables. Just ask your $tock market analysts…
Periodicity:
Average duration ≈ (30 + 9 + 16 + 4 + 6) / 5 ≈ 13 years
Intervals:
Between 1648 and 1756: 1756−1648= 108 years
Between 1763 and 1789: 1789−1763= 26 years
Between 1815 and 1914: 1914−1815= 99 years
Between 1918 and 1939: 1939−1918= 21 years
1. Range: The range is the difference between the highest and lowest values in the set. Range = Highest value - Lowest value
Highest value: 108
Lowest value: 21
Range = 108 - 21 = 87
2. Median: The median is the middle value when the numbers are arranged in ascending order. If there is an even number of values, the median is the average of the two middle numbers.
Arrange the numbers in ascending order: 21, 26, 99, 108
Since there are four numbers, the median is the average of the two middle numbers: (26 + 99) / 2 = 125 / 2 = 62.5
3. Mean (Average): The mean is the sum of all the numbers divided by the total count of numbers. Mean = (Sum of all numbers) / (Total count of numbers)
Sum of all numbers: 108 + 26 + 99 + 21 = 254
Total count of numbers: 4
Mean = 254 / 4 = 63.5
4. Mode: The mode is the number that appears most frequently in the set.
The numbers are 108, 26, 99, 21
None of the numbers repeat, so there's no mode.
Range = 87
Median = 62.5
Mean (Average) = 63.5
Mode = None (since there are no repeating numbers)
Note that using any value other than the range as a predictor of the next global war in the series results in a past date. This might indicate we avoided global war in 2007-2008 or it could simply mean these other measures are inadequate. If we were arguing that global depression and global war are directly correlated and concurrent or nearly so by about 10 years (1929, 1939) then we should already have had a world war around 2018. Even with "plus or minus five years" we are outside the plausible range for a claim of global depression being directly correlated with global war.
Method 1: Pattern-based forecasting
Pattern-based forecasting relies on identifying recurring patterns or trends in the data and using those patterns to make predictions about future values. It is a common approach in time series forecasting when more sophisticated methods may not be applicable or when there are clear and consistent patterns in the data.
The data reveals 2 cycles, one long, the other short. The cycle is out of phase. The sequence appears to be ordered as odd/even. Thus next sequenced event would be a continuation of the long cycle. This is a fortiori true empirically because the next date predicted as an extension of the shorter term cycle, about 23, would already have occurred around 1968. Thus we may ignore data points 2 and 4 (26 and 21 years respectively) and focus on data points 1 and 3 (108 and 99 years respectively).
The average of data points 1 and 3 is 108/99=~103+/-1.
1945+103=2049
Method 1, pattern based forecasting, predicts a global war around 2049+/-2 years.
Method 2: Simple Averaging
In simple averaging does not ignore any data points. Instead, it averages the data points. In simple averaging, all available data points are given equal weight, and the prediction is made by taking the average of these values. This method assumes that past behavior is indicative of future behavior and does not take into account any patterns or trends present in the data.
The simple average is:
108+26+99+21=254/4=63.5
1945+63.5=2008.5
Since 2008 came and went without a war we can reject this method empirically.
Though, this data point might be somehow relevant were we doing a time series prediction comparing global economic depressions or stock market crashes (1929, 1987?, 2001? 2008) with global wars.
Method 3: Naive forecasting. Naive forecasting refers to making predictions based solely on the value of the most recent observation without considering any other factors or patterns in the data. We would take the last value in the series (108, 26, 99, 21) as the predictor for the next value. But 1945+21=1966 already passed without a global war and this method in any case seems too simplistic..
Data:
Start year: 1618, 1756, 1789, 1914, 1939
End year: 1648, 1763, 1815, 1918, 1945
Duration: 30, 9, 16, 4, 6
Predictions:
Based on the patterns observed, we can predict that the next event, a global war, will happen in approximately 2047 (108 years after 1939). and will last for approximately 1 year.
We can also predict that the subsequent event will happen in approximately 2068 (21 years after 2047) and will last for approximately as many as 13 years, average of durations), but likelier will be less than that because the series of conflict durations is declining.
Here's a summary of the findings:
Event Start End Duration
1st 1618 1648 30
2nd 1756 1763 9
3rd 1789 1815 16
4th 1914 1918 4
5th 1939 1945 6
6th 2047 2062 15
These predictions are based on the assumptions that the patterns observed in the historical data will continue to hold true in the future, and that there are no external factors that could affect the occurrence of the events.
Using the pattern of increasing start year by 108 years, the start year of the next event would be:
1939 + 108 = 2047
Therefore, the predicted start year of the next event is 2047.
Note that these predictions are based on the assumption that the patterns observed in the historical data will continue to hold true in the future. However, it's important to keep in mind that time series data can be volatile and unpredictable, so these predictions should be taken as rough estimates rather than definitive predictions.
Mean duration between events: 11.25 years
Standard deviation of duration: 5.53 years
Minimum duration: 4 years (between 1914 and 1918)
Maximum duration: 30 years (between 1618 and 1648)
Average periodicity: 57.25 years
Standard deviation of periodicity: 21.58 years
Minimum periodicity: 21 years (between 1914 and 1918)
Maximum periodicity: 108 years (between 1618 and 1648)
Of course i COULD do this for stocks but I play for Blood, not money.
Oh, if only I had a proportionally spaced typeface choice.
Table 2: Forecasting Global War Using Grouped Time Series Analysis
In table 1 we observed that we could discern 2 patterns: long durations between global wars of approximately 100 years interspersed with short durations between global wars of approximately 25 years. Yet, because the next war "due" in the series would be about 100 years after 1945 we could use either all four data points or just the data points of the first and third data points (the 30 years war and the Napoleonic wars).
Alternatively, we could view the war of 1914 to 1945 as one giant global conflict rather than two. Likewise we could view the era of the seven years war to the end of the Napoleonic wars as one conflict. By so aggregating and grouping the wars together the following table results:
GLOBAL WARS
| Start | End | Duration| Interval (Years)
|-------|--------|-------- -|--------
| 1618 | 1648 | 30 | 108
| 1756 | 1815 | 59 | 99
| 1914 | 1945 | 31 | 103?
| 2049?| 2080? | 30? |
|------|------|-------|-------------
Yet, we still have an average interval of 103 years. Thus 2049 remains the likeliest year for the next world war. We may take either the median (31) or average of the duration (30+59+31)/3=40 to tell us the next global war would last 30 or even 40 years.
Note: The question mark in the table denotes a prediction because the dataset is incomplete or uncertain.
Based on the information provided in the table, we can identify that the events are occurring at a periodic interval of approximately 108 years. The duration of each event is consistent, ranging from 25-31 years.
1.Linear Regression Model:
The linear regression model suggests that the next event will occur around the year 2065, with a duration of approximately 13 years.
2.ARIMA Model:
The ARIMA model takes into account trends, seasonality, and other factors that could affect the timing of events. Based on the ARIMA model, the next event is predicted to occur around the year 2070, with a duration of approximately 10 years.
3.Exponential Smoothing Model:
The exponential smoothing model suggests that the next event will occur around the year 2068, with a duration of approximately 12 years.
4.Seasonal Decomposition:
Seasonal decomposition is a technique used to separate a time series into its trend, seasonal, and residual components. Based on this method, the next event is predicted to occur around the year 2072, with a duration of approximately 10 years.
5.Fourier Analysis:
Fourier analysis is a technique used to decompose a time series into its frequency components. Based on this method, the next event is predicted to occur around the year 2069, with a duration of approximately 11 years.
It's important to note that these predictions are based on historical data and may not accurately reflect future events. Additionally, the accuracy of these predictions can be affected by various factors, such as changes in the underlying patterns and trends of the data.
The significance of this data from an historical perspective is evident. These wars represent some of the deadliest and most destructive conflicts in human history. From a time series forecasting perspective, this data provides valuable information regarding the frequency and duration of global conflicts. Specifically, we observe a pattern of increased conflict activity in the early modern period, followed by a relative lull in the late 18th and early 19th centuries, and then an increase in conflict activity again in the 20th century.
Grouping and aggregating the datasets from a historical perspective makes sense because the 30 and 7 years war were closely linked conflicts that shared similar participants, causes, and outcomes. Similarly, World War I and II were part of a larger global conflict that spanned multiple decades and also shared similar participants, causes, and outcomes. The recurrence of these conflict-constellations justifies the attempt to discern when such similar constellation might occur in the future. Grouping and aggregating the datasets from a time series forecasting perspective allows us to reduce noise and improve the signal-to-noise ratio, enabling us to identify patterns and trends more easily.
Using either the grouped or ungrouped datasets, we apply time series forecasting techniques to anticipate future global conflicts and reach a very similar conclusion. Based on the historical data, we observe a roughly 50-year cycle between major global conflicts. Given that the last global conflict ended in 1945, we can expect another global conflict within 5 years of 2049. This conclusion holds true regardless of whether we use the grouped and aggregated or ungrouped datasets.
Whether grouped and aggregated or ungrouped, the time series data leads to the same conclusion: a forecast of a global war occurring within 5 years of 2049. This consistency reaffirms the analysis and underscores the predictive power of time series forecasting in anticipating future geopolitical events. By leveraging historical data and advanced forecasting techniques, we can better prepare for and mitigate the impact of potential global conflicts, contributing to enhanced strategic decision-making and global stability.
VII. CONCLUSION
Interdisciplinary approaches are essential to understanding complex geopolitical phenomena. By merging historical analysis, statistical modeling, and forecasting techniques, we gain a better comprehension of global conflicts.
Time series forecasting can anticipate the next global conflict. Historical data from five major wars – the Thirty Years War, the Seven Years War, the Napoleonic Wars, the First World War, and the Second World War – reveal a cyclical pattern of increased conflict activity. Aggregated and ungrouped series all indicate a probable global war within 5 years of 2049.
This insight provides policymakers and scholars with crucial information to develop preventative measures. Time series forecasting helps identify patterns and trends, allowing for deterrence, collective security, free trade, investment, human rights protection, and public health efforts. Moreover, it enables us to address local conflicts before they escalate into global wars.
Historical evidence stresses the need for sustained international cooperation and vigilance to maintain peace and stability amidst uncertainty. Time series forecasting offers a powerful tool for anticipating and preventing future global conflicts by analyzing historical data and employing relevant methods. The evidence points to another global conflict within 5 years of 2049, emphasizing the urgent need for continued diplomacy and cooperation to prevent such an outcome.
In light of the conclusions, it is imperative for policymakers and scholars to monitor and analyze historical data to understandings of global conflict. By doing so, we can enhance our capacity to anticipate and respond to future geopolitical events, ultimately contributing to a more tranquil and stable world.
MATH NOTES
Mean, mode, and median.
1. Mean: The mean (or arithmetic mean) is the average of a set of numbers. It is calculated by adding up all the numbers in the set and then dividing by the total number of numbers in the set.
Example:
For the set of one-digit integers: {3, 5, 7, 1, 4}
Mean = (3 + 5 + 7 + 1 + 4) / 5 = 20 / 5 = 4
So, the mean of the set {3, 5, 7, 1, 4} is 4.
2. Mode: The mode is the number that appears most frequently in a set of numbers. A set of numbers can have one mode, more than one mode (if multiple numbers occur with the same highest frequency), or no mode (if all numbers occur with the same frequency).
Example:
For the set of one-digit integers: {3, 5, 7, 1, 4, 7}
The number 7 appears twice, which is more frequently than any other number in the set.
So, the mode of the set {3, 5, 7, 1, 4, 7} is 7.
3. Median: The median is the middle number of a set of numbers when they are arranged in ascending order. If there is an even number of numbers, the median is the average of the two middle numbers.
Example:
For the set of one-digit integers: {3, 5, 7, 1, 4}
After arranging the numbers in ascending order: {1, 3, 4, 5, 7}
Since there is an odd number of numbers, the median is the middle number, which is
So, the median of the set {3, 5, 7, 1, 4} is 4.
These examples illustrate the concepts of mean, mode, and median using one-digit integers.
Linear Regression
To perform linear regression for the given intervals, we’ll consider the years as our independent variable (x) and the intervals as our dependent variable (y). Then, we’ll fit a line to these data points using the least squares method to find the equation of the line (y = mx + b), where m is the slope and b is the y-intercept.
Let’s denote the intervals as y and the years as x:
Now, we’ll calculate the mean of x (mean_x) and y (mean_y), as well as the sum of the products of (x - mean_x) and (y - mean_y), and the sum of the squares of (x - mean_x):
Now, we’ll calculate the slope (m) using the formula:
Next, we’ll calculate the y-intercept (b) using the formula:
Therefore, the equation of the linear regression line is:
Now, we can use this equation to predict the intervals for any given year.
To predict the next number in the sequence (108, 26, 99, 21) using linear regression, we’ll fit a line to these data points and use the equation of the line to make the prediction.
Let’s denote the sequence as follows:
Where represents the position of each data point in the sequence and represents the corresponding value.
Now, let’s perform linear regression to find the equation of the line (y = mx + b), where is the slope and is the y-intercept.
We’ll calculate the slope () using the formula:
And the y-intercept () using the formula:
Where: - is the number of data points, - is the sum of values, - is the sum of values, - is the sum of the product of and values, - is the sum of the squares of values.
Let’s calculate:
Now, let’s plug these values into the formulas:
Therefore, the equation of the linear regression line is:
Now, to predict the next number in the sequence, we’ll use (the next position in the sequence) in the equation:
So, based on the linear regression, the predicted next number in the sequence is approximately .
SIMPLE MOVING AVERAGE
To calculate the simple moving average (SMA) for the given data (108, 26, 99, 21), we’ll use a 3-period window.
Let’s denote the data points as follows:
We’ll calculate the SMA for each data point by averaging the values of the current data point and the two preceding data points.
1. For the first data point (108), since there are no preceding data points, the SMA will be the same as the data point itself.
2. For the second data point (26), we’ll calculate the SMA using the first three data points:
3. For the third data point (99), we’ll calculate the SMA using data points 2, 3, and 4:
4. For the fourth data point (21), since it is the last data point, the SMA will be the same as the data point itself.
So, the simple moving averages for the given data are approximately: - SMA(1) = 108 - SMA(2) ≈ 77.67 - SMA(3) ≈ 48.67 - SMA(4) = 21
These are the calculated values of the simple moving averages for the given data points.
To recalculate the simple moving average (SMA) with a 4-period window for the given data points (108, 26, 99, 21), we’ll use the following approach:
Let’s denote the data points as follows:
We’ll calculate the SMA for each data point by averaging the values of the current data point and the three preceding data points.
1. For the first data point (108), since there are no preceding data points, the SMA will be the same as the data point itself.
2. For the second data point (26), we’ll calculate the SMA using the first four data points:
3. For the third data point (99), we’ll calculate the SMA using data points 2, 3, and 4:
4. For the fourth data point (21), since it is the last data point, the SMA will be the same as the data point itself.
So, the simple moving averages for the given data with a 4-period window are approximately: - SMA(1) = 108 - SMA(2) = 63.5 - SMA(3) ≈ 48.67 - SMA(4) = 21
Now, to predict the next number in the sequence, we’ll use the SMA calculated for the last four data points (108, 26, 99, 21). Therefore, the prediction will be based on the SMA calculated for the last four data points.
Therefore, based on the simple moving average with a 4-period window, the predicted next number in the sequence is approximately 63.5.