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3 | <html xmlns="http://www.w3.org/1999/xhtml" xml:lang="en" lang="en"> |
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5 | <title>a_timecorrelate.pro (SAXO Documentation Assistant)</title> |
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45 | <table border="0" cellpadding="0" cellspacing="0" width="98%" bgcolor="#F0F0FF" valign="bottom"> |
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46 | <tr> |
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47 | <td width="10%"> |
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48 | <a href="a_correlate2d.html"><img src="./../../prev.gif" border="0" alt="Previous"></a></td> |
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49 | <td width="80%" align="center" valign="center"> |
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50 | <font size=-1><i>SAXO Documentation Assistant</i>: <a href="./../../home.html">Overview</a></font></td> |
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51 | <td width="10%" align="right"> |
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52 | <a href="c_timecorrelate.html"><img src="./../../next.gif" border="0" alt="Next"></a></td> |
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53 | </tr> |
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54 | </table> |
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55 | |
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56 | |
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57 | <h1><font size="-2">ToBeReviewed/STATISTICS/</font></h1> |
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58 | <h2>a_timecorrelate.pro</h2> |
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59 | |
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60 | <dl> |
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61 | </dl> |
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62 | |
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63 | |
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64 | |
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65 | |
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66 | Same function as <a href="/Applications/itt/idl64/help/online_help/A_CORRELATE.html">A_CORRELATE</a> but accept array (until 4 |
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67 | dimension) for input and do the autocorrelation or the |
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68 | autocovariance along the time dimension which must be the last |
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69 | one of the input array. |
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70 | |
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71 | This function computes the autocorrelation Px(L) or autocovariance |
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72 | Rx(L) of a sample population X as a function of the lag (L). |
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73 | |
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74 | |
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75 | |
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76 | |
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77 | |
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78 | <a name="#_a_timecorrelate"></a> |
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79 | |
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80 | <h2>a_timecorrelate <font size="-1" color="#006633"> |
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81 | Statistics |
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82 | </font></h2> |
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83 | |
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84 | <p><font face="Courier"><i>result = </i>a_timecorrelate(<i><a href="#_a_timecorrelate_keyword_x">x</a>, <a href="#_a_timecorrelate_keyword_lag">lag</a></i>, <a href="#_a_timecorrelate_keyword_COVARIANCE">COVARIANCE</a>=<i>COVARIANCE</i>, <a href="#_a_timecorrelate_keyword_DOUBLE">DOUBLE</a>=<i>DOUBLE</i>, <a href="#_a_timecorrelate_keyword_NVAL">NVAL</a>=<i>NVAL</i>)</font></p> |
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85 | |
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86 | |
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87 | |
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88 | |
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89 | |
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90 | |
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91 | |
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92 | <h3>Parameters</h3> |
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93 | |
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94 | |
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95 | <a name="#_a_timecorrelate_keyword_x"></a> |
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96 | <h4>x |
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97 | <font size="-1" color="#006633">in</font> |
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98 | |
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99 | |
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100 | <font size="-1" color="#006633">required</font> |
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101 | |
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102 | <font size="-1" color="#006633">type:</font> <font size="-1" color="#006633"><i>array</i></font> |
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103 | |
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104 | |
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105 | </h4> |
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106 | |
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107 | |
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108 | An array which last dimension is the time dimension so |
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109 | size n. |
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110 | |
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111 | |
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112 | |
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113 | <a name="#_a_timecorrelate_keyword_lag"></a> |
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114 | <h4>lag |
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115 | <font size="-1" color="#006633">in</font> |
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116 | |
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117 | |
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118 | <font size="-1" color="#006633">required</font> |
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119 | |
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120 | <font size="-1" color="#006633">type:</font> <font size="-1" color="#006633"><i>scalar or vector</i></font> |
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121 | |
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122 | |
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123 | </h4> |
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124 | |
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125 | |
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126 | A scalar or n-element vector, in the interval [-(n-2), (n-2)], |
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127 | of type integer that specifies the absolute distance(s) between |
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128 | indexed elements of X. |
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129 | |
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130 | |
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131 | |
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132 | |
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133 | |
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134 | <h3>Keywords</h3> |
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135 | |
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136 | |
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137 | <a name="#_a_timecorrelate_keyword_COVARIANCE"></a> |
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138 | <h4>COVARIANCE |
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139 | |
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140 | |
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141 | |
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142 | |
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143 | |
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145 | |
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146 | |
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147 | </h4> |
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148 | |
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149 | |
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150 | If set to a non-zero value, the sample autocovariance |
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151 | is computed. |
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152 | |
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153 | |
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154 | <a name="#_a_timecorrelate_keyword_DOUBLE"></a> |
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155 | <h4>DOUBLE |
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156 | |
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157 | |
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158 | |
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159 | |
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160 | |
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161 | |
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162 | |
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163 | |
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164 | </h4> |
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165 | |
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166 | |
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167 | If set to a non-zero value, computations are done in |
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168 | double precision arithmetic. |
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169 | |
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170 | |
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171 | <a name="#_a_timecorrelate_keyword_NVAL"></a> |
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172 | <h4>NVAL |
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173 | |
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174 | |
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175 | |
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176 | |
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177 | |
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178 | |
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179 | |
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180 | |
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181 | </h4> |
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182 | |
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183 | |
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184 | A named variable that, on exit, contains the number of valid |
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185 | observations (not NAN) |
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186 | |
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187 | |
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188 | |
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189 | |
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190 | <h3>Examples</h3><pre> |
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191 | Define an n-element sample population. |
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192 | IDL> x = [3.73, 3.67, 3.77, 3.83, 4.67, 5.87, 6.70, 6.97, 6.40, 5.57] |
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193 | |
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194 | Compute the autocorrelation of X for LAG = -3, 0, 1, 3, 4, 8 |
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195 | IDL> lag = [-3, 0, 1, 3, 4, 8] |
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196 | IDL> result = a_correlate(x, lag) |
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197 | |
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198 | The result should be: |
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199 | [0.0146185, 1.00000, 0.810879, 0.0146185, -0.325279, -0.151684] |
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200 | |
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201 | </pre><h3>Version history</h3> |
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202 | |
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203 | <h4>Version</h4> |
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204 | $Id: a_timecorrelate.pro 335 2008-03-07 15:09:25Z smasson $ |
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205 | |
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206 | <h4>History</h4> |
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207 | 24/2/2000 Sebastien Masson (smasson@lodyc.jussieu.fr) |
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208 | |
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209 | Based on the A_CORRELATE procedure of IDL |
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210 | INTRODUCTION TO STATISTICAL TIME SERIES |
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211 | Wayne A. Fuller |
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212 | ISBN 0-471-28715-6 |
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230 | <font size="-3"><p> </p></font> |
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231 | <hr size="1" color="#CCCCCC"/> |
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236 | <p><font color="gray" size="-3"> Produced by IDLdoc 2.0.</font></p> |
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237 | |
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238 | </body> |
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239 | </html> |
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