In　understanding　Big　Data,　people　are　interested　to　obtain　the　trend　and　dynamics　of　a　given　set　of　temporal　data,　which　in　turn　can　be　used　to　predict　possible　futures.　This　paper　examines　a　time　series　analysis　method　and　an　ordinary　differential　equation　approach　in　modeling　the　price　movements　of　petroleum　price　and　of　three　different　bank　stock　prices　over　a　time　frame　of　three　years.　Computational　tests　consist　of　a　range　of　data　fitting　models　in　order　to　understand　the　advantages　and　disadvantages　of　these　two　approaches.　A　modified　ordinary　differential　equation　model,　with　different　forms　of　polynomials　and　periodic　functions,　is　proposed.　Numerical　tests　demonstrated　the　advantage　of　the　modified　ordinary　differential　equation　approach.　Computational　properties　of　the　modified　ordinary　differential　equation　are　studied.　©　2018,　©　The　Author(s)　2018.