100 Examples of sentences containing the noun "autocorrelation"

Definition

Autocorrelation refers to the correlation of a signal with a delayed copy of itself as a function of the delay. In statistics, it is a measure of how a variable is correlated with itself over successive time intervals.

Synonyms

• Serial correlation
• Lagged correlation
• Self-correlation

Antonyms

• Uncorrelated
• Independent

Examples

1. The analysis revealed that the time series showed significant autocorrelation in its early stages.
2. Researchers often use autocorrelation to determine the predictability of financial data.
3. The model's effectiveness is assessed by examining its autocorrelation properties.
4. In this study, we found that autocorrelation can lead to misleading results if ignored.
5. The presence of autocorrelation in the residuals suggests a need for model adjustment.
6. Time series analysis often starts with computing the autocorrelation function.
7. A high level of autocorrelation implies that past values heavily influence future values.
8. Autocorrelation can be used to identify cyclical patterns in economic data.
9. The autocorrelation coefficient indicates the strength of the relationship between values at different times.
10. Adjusting for autocorrelation is crucial in forecasting models.
11. The test for autocorrelation showed that the data was stationary.
12. Autocorrelation can affect the validity of statistical tests if not addressed.
13. In this experiment, we measured the autocorrelation of sound waves.
14. Understanding autocorrelation helps in refining predictive algorithms.
15. The autocorrelation function is used to detect the presence of patterns in time series.
16. We calculated the autocorrelation of the temperature data over the past decade.
17. The results indicated that there was no significant autocorrelation in the dataset.
18. Autocorrelation plays a crucial role in signal processing applications.
19. A positive autocorrelation indicates that high values are followed by high values.
20. We explored various methods to mitigate autocorrelation in our regression analysis.
21. The autocorrelation plot provided insights into the underlying data structure.
22. Detecting autocorrelation is essential for ensuring the independence of observations.
23. The autocorrelation analysis confirmed our initial hypothesis about the data trends.
24. In time series forecasting, autocorrelation can enhance model accuracy.
25. The presence of significant autocorrelation was evident in the sales data.
26. We utilized a statistical test to assess the autocorrelation of the residuals.
27. The autocorrelation matrix revealed interesting relationships among variables.
28. Autocorrelation can indicate whether a time series is stationary or non-stationary.
29. A negative autocorrelation suggests that high values are followed by low values.
30. We visualized the autocorrelation structure using a correlogram.
31. Autocorrelation was a key factor in our time series analysis.
32. The research focused on the autocorrelation of rainfall patterns.
33. By analyzing autocorrelation, we could better understand the underlying processes.
34. The presence of autocorrelation may complicate the estimation of model parameters.
35. Adjustments for autocorrelation improved the reliability of our forecasts.
36. The autocorrelation function helped us identify seasonal effects in the data.
37. We found that autocorrelation was significant at lag 1 in our analysis.
38. The implications of autocorrelation were discussed in the context of economic modeling.
39. A thorough examination of autocorrelation can enhance data interpretation.
40. The autocorrelation of stock prices was analyzed over multiple time horizons.
41. The study focused on the implications of autocorrelation for policy decisions.
42. The researchers applied autocorrelation techniques to their longitudinal data.
43. Significant autocorrelation was noted in the behavioral data collected.
44. We used autocorrelation to validate our predictive model.
45. The autocorrelation coefficients were plotted for better visualization.
46. Evaluating autocorrelation is essential for accurate time series modeling.
47. The autocorrelation analysis indicated a strong temporal dependency in the data.
48. The implications of autocorrelation in environmental studies were examined.
49. The residuals exhibited clear signs of autocorrelation after the initial analysis.
50. Autocorrelation can provide insights into the cyclic nature of certain phenomena.
51. The study highlighted the effects of autocorrelation on statistical inference.
52. We computed the autocorrelation for various lags to understand the data better.
53. The method used to assess autocorrelation was robust and reliable.
54. In time series data, autocorrelation can indicate systematic patterns.
55. The results showed that the autocorrelation diminished quickly over time.
56. Analyzing autocorrelation is fundamental in econometric modeling.
57. The researchers sought to determine the autocorrelation structure of the data.
58. A thorough understanding of autocorrelation can enhance model performance.
59. The autocorrelation function revealed significant lags in the data.
60. We examined the potential effects of autocorrelation on our findings.
61. The statistical test for autocorrelation was conducted on the dataset.
62. Identifying autocorrelation is crucial for improving data-driven decisions.
63. The autocorrelation findings were consistent across multiple datasets.
64. We conducted a detailed analysis of autocorrelation in the observed trends.
65. The effects of autocorrelation on time-to-event data were investigated.
66. Autocorrelation can significantly impact the conclusions drawn from the analysis.
67. The study provided a comprehensive overview of autocorrelation methods.
68. We found that the autocorrelation patterns changed over time.
69. The analysis of autocorrelation was integral to understanding the data dynamics.
70. The plot illustrated the autocorrelation of the measured variables clearly.
71. In assessing autocorrelation, we considered various statistical methodologies.
72. The researchers noted that autocorrelation could lead to biased estimates.
73. Various models were tested for their ability to handle autocorrelation.
74. The autocorrelation function is a common tool in time series analysis.
75. We explored the relationship between autocorrelation and volatility.
76. The implications of autocorrelation for machine learning algorithms were discussed.
77. Significant levels of autocorrelation can indicate underlying issues in data collection.
78. The presence of autocorrelation can signal the need for different analytical approaches.
79. We assessed the impact of autocorrelation on the regression results.
80. Understanding autocorrelation is vital for accurate time series forecasting.
81. The autocorrelation of the economic indicators was analyzed rigorously.
82. A high degree of autocorrelation may suggest a need for model adjustment.
83. The study aimed to quantify the effects of autocorrelation on policy analysis.
84. We utilized autocorrelation to identify trends and cycles in the data.
85. The analysis revealed that the autocorrelation was stronger in the winter months.
86. The researchers emphasized the importance of addressing autocorrelation.
87. In their findings, autocorrelation was a critical factor influencing outcomes.
88. The team implemented methods to reduce autocorrelation in their models.
89. The results showed an unexpected autocorrelation pattern in the results.
90. Autocorrelation analysis can enhance understanding of temporal phenomena.
91. We explored how autocorrelation influences various modeling approaches.
92. The data exhibited strong autocorrelation at certain lags.
93. The implications of autocorrelation for data integrity were examined.
94. We highlighted the significance of accounting for autocorrelation in research.
95. The presence of autocorrelation necessitated a re-evaluation of the model.
96. Understanding the autocorrelation structure can improve decision-making processes.
97. The study focused on the role of autocorrelation in predictive analytics.
98. We conducted a thorough investigation into the autocorrelation levels in the data.
99. The researchers pointed out the challenges posed by autocorrelation.
100. Addressing autocorrelation is essential for achieving valid statistical results.